Overview of the LLaMA-Omni2 Architecture

LLaMA-Omni2 encompasses models ranging from 0.5B to 14B parameters, each built atop the Qwen2.5-Instruct series. The architecture consists of:

• Speech Encoder: Utilizes Whisper-large-v3 to transform input speech into token-level acoustic representations.
• Speech Adapter: Processes encoder outputs using a downsampling layer and a feed-forward network to align with the language model’s input space.
• Core LLM: The Qwen2.5 models serve as the main reasoning engine.
• Streaming TTS Decoder: Converts LLM outputs into speech tokens using an autoregressive Transformer and then generates mel spectrograms through a causal flow matching model inspired by CosyVoice2.

A gating mechanism fuses LLM hidden states with textual embeddings before speech synthesis, enhancing contextual fidelity in the generated audio.

Streaming Generation with Read-Write Scheduling

The model adopts a read-write strategy to facilitate streaming output. Specifically, for every R tokens produced by the LLM, W speech tokens are generated. This enables synchronized textual and acoustic generation, minimizing latency without compromising fluency.

Empirical findings suggest that setting R = 3 and W = 10 provides a favorable trade-off between latency (~583 ms), alignment (ASR-WER: 3.26), and perceptual quality (UTMOS: 4.19).

Training Approach

Despite achieving competitive performance, LLaMA-Omni2 is trained on a relatively compact corpus—200K multi-turn speech-to-speech dialogue samples. These samples are synthesized from instruction-following text datasets (Alpaca, UltraChat), with diverse input voices and a consistent output voice generated using FishSpeech and CosyVoice2 models.

Training is executed in two stages:

• Stage I: Independently optimizes the speech-to-text and text-to-speech modules.
• Stage II: Fine-tunes the speech-to-speech generation path, including the gating and autoregressive decoding components.

Benchmark Results

The models are evaluated on spoken question answering and speech instruction following tasks using both speech-to-text (S2T) and speech-to-speech (S2S) modes.

The performance scales consistently with model size. Notably, LLaMA-Omni2-14B outperforms all baselines across tasks, even with substantially less training data than native SpeechLMs such as GLM-4-Voice.

Component Analyses

• Gate Fusion Module: Removing the gating mechanism increases ASR-WER and reduces speech quality, confirming its role in aligning textual and contextual signals.
• TTS Pretraining: Initializing the TTS model from Qwen2.5 and fine-tuning in a streaming setup yields the best performance. Training from scratch fails to converge effectively.
• Read/Write Strategies: Adjusting the R:W ratio impacts latency and quality. Larger W improves UTMOS but at the cost of response delay.

Additionally, the study demonstrates that multi-turn dialogue data is more effective than single-turn data in training speech interaction capabilities, and that performance plateaus around 200K samples.

Conclusion

LLaMA-Omni2 demonstrates that high-quality, low-latency spoken interaction with LLMs is feasible without the need for extensive pretraining on massive speech corpora. By combining modular architecture with autoregressive streaming synthesis, the system offers a practical pathway for real-time speech applications.

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The post LLMs Can Now Talk in Real-Time with Minimal Latency: Chinese Researchers Release LLaMA-Omni2, a Scalable Modular Speech Language Model appeared first on MarkTechPost.

Furthermore, existing surveys tend to address only specific subtopics within the broader memory landscape, such as long-context handling, long-term memory, personalization, or knowledge editing. These fragmented approaches often miss essential operations like indexing and fail to offer comprehensive overviews of memory dynamics. Additionally, most prior work does not establish a clear research scope or provide structured benchmarks and tool coverage, limiting their practical value for guiding future advancements in memory for AI systems. 

Researchers from the Chinese University, the University of Edinburgh, HKUST, and the Poisson Lab at Huawei UK R&D Ltd. present a detailed survey on memory in AI systems. They classify memory into parametric, contextual-structured, and contextual-unstructured types, distinguishing between short-term and long-term memory inspired by cognitive psychology. Six fundamental operations—consolidation, updating, indexing, forgetting, retrieval, and compression—are defined and mapped to key research areas, including long-term memory, long-context modeling, parametric modification, and multi-source integration. Based on an analysis of over 30,000 papers using the Relative Citation Index, the survey also outlines tools, benchmarks, and future directions. 

The researchers first develop a three‐part taxonomy of AI memory—parametric (model weights), contextual‐structured (e.g., indexed dialogue histories), and contextual‐unstructured (raw text or embeddings)—and distinguish short‐ versus long‐term spans. They then define six core memory operations: consolidation (storing new information), updating (modifying existing entries), indexing (organizing for fast access), forgetting (removing stale data), retrieval (fetching relevant content), and compression (distilling memories). To ground this framework, they mined over 30,000 top‐tier AI papers (2022–2025), ranked them by Relative Citation Index, and clustered high‐impact works into four themes—long‐term memory, long‐context modeling, parametric editing, and multi‐source integration—thereby mapping each operation and memory type to active research areas and highlighting key benchmarks and tools. 

The study describes a layered ecosystem of memory-centric AI systems that support long-term context management, user modeling, knowledge retention, and adaptive behavior. This ecosystem is structured across four tiers: foundational components (such as vector stores, large language models like Llama and GPT-4, and retrieval mechanisms like FAISS and BM25), frameworks for memory operations (e.g., LangChain and LlamaIndex), memory layer systems for orchestration and persistence (such as Memary and Memobase), and end-user-facing products (including Me. bot and ChatGPT). These tools provide infrastructure for memory integration, enabling capabilities like grounding, similarity search, long-context understanding, and personalized AI interactions.

The survey also discusses open challenges and future research directions in AI memory. It highlights the importance of spatio-temporal memory, which balances historical context with real-time updates for adaptive reasoning. Key challenges include parametric memory retrieval, lifelong learning, and efficient knowledge management across memory types. Additionally, the paper draws inspiration from biological memory models, emphasizing dual-memory architectures and hierarchical memory structures. Future work should focus on unifying memory representations, supporting multi-agent memory systems, and addressing security concerns, particularly memory safety and malicious attacks in machine learning techniques. 

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Linear complexity language models have emerged as alternatives to transformer-based architectures that suffer from quadratic computational demands when processing long sequences. The RWKV model series combines transformer parallelizability during training with RNN-like recurrent state representation. RWKV has evolved through multiple iterations, from the foundational RWKV-4 to RWKV-5 to RWKV-6 to RWKV-7. Hybrid language models, including Jamba, Zamba, and MiniMax, enhance hybrid designs uniquely. Further, Native Sparse Attention organizes tokens into temporal blocks with three distinct attention paths: compressed coarse-grained tokens, selectively retained fine-grained tokens, and sliding windows for local contextual information. Other attention includes SeerAttention and Block Attention (MoBA).

Researchers from Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, Hohai University, Nanjing, Shenzhen University, and Qinghai University, Xining, have proposed a novel hybrid architecture called RWKV-X that combines RWKV’s efficiency for short-range modeling with a sparse attention mechanism designed to capture long-range context. Unlike previous hybrid approaches, RWKV-X achieves linear-time complexity during training and constant-time complexity during inference decoding. It shows near-perfect accuracy on the 64K passkey retrieval benchmark when pretrained on 64K-token sequences continuously. The model consistently outperforms previous RWKV-7 models on long-context benchmarks while maintaining strong performance on short-context tasks.

RWKV-X is a hybrid architecture that integrates RWKV-7 blocks with sparse attention blocks. Rather than training from scratch, RWKV-X builds upon existing models using an interleaved block expansion approach and zero-initialization mechanism inspired by LLaMA Pro. The training follows a two-stage process:

• First, the model trains on short 1024-token contexts from the MiniPile dataset while freezing all parameters except the newly added blocks. 
• The second stage involves long-context continual pretraining using the ProLong-64K dataset and a context length of 64K tokens, processing approximately 1 billion tokens total. During this phase, all parameters are unfrozen and jointly optimized. The training employs Long-context Cross-Entropy (LongCE) loss, which dynamically weights tokens based on their importance.

The Short-context evaluation reveals that RWKV-X maintains competitive performance across standard benchmarks. The smaller RWKV-X (0.22B) achieves an average score of 51.0, comparable to RWKV-7’s 51.8. At a larger scale, RWKV-X (3.6B) reaches 71.9, closely matching RWKV-7 (2.9B, 72.8) and Qwen2.5-3B (71.4), while surpassing LLaMA3.2-3B (69.7). These results confirm RWKV-X’s effectiveness as a general-purpose LLM backbone without sacrificing performance on shorter contexts. Moreover, efficiency analysis demonstrates RWKV-X’s superior scaling characteristics for long sequences. At 128K tokens, RWKV-X achieves a 1.37 times speedup over Flash-Attention v3, with this advantage expanding as context length increases.

In this paper, researchers introduced RWKV-X, which emerges as a hybrid language model that successfully combines RWKV’s efficiency for modeling short-range dependencies with a novel sparse attention mechanism designed specifically for long-range context modeling. While RWKV-X demonstrates strong performance and efficiency in long-context language modeling, several limitations remain. First, its sparse attention mechanism, which relies on top-k chunk selection, employs a heuristic approach that may overlook semantically relevant dependencies. Second, the current implementation shows sparse attention decoding running slower than vanilla RWKV, indicating that further engineering efforts are needed to optimize performance.

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Evolution of Reasoning in LLMs

The development of Chain-of-Thought (CoT) methodology marked a significant advancement in LLM reasoning capabilities. CoT has demonstrated substantial improvements across mathematics, science, and programming domains by incorporating multi-step intermediate reasoning processes before reaching conclusions. This approach allows models to break down complex problems into manageable steps, mirroring human problem-solving processes.

While mathematical reasoning has dominated recent research due to its verifiable nature, the expansion of RL training to diverse domains remains largely unexplored. Prior research works suggest that blending mathematical content with other verifiable domains can improve performance on broad reasoning benchmarks. However, systematic investigation into how non-mathematical reasoning data, such as legal analysis, social science, or historical interpretation, impacts RL training effectiveness still represents a significant research gap.

Challenges in Diversifying Reasoning Domains

Recent research has explored methods for diversifying RL training datasets, yet questions about optimal data-blending strategies and the relative importance of various sources remain unanswered. A fundamental challenge in applying RL to general reasoning tasks is developing verifiable reward models for domains lacking deterministic solutions. Domain-specific reasoning processes—whether rule-based and symbolic in mathematics or contextual and heuristic in fields like law and history—require different cognitive approaches. In addition to that, question formats (open-ended versus multiple-choice) demand distinct reasoning strategies, suggesting that incorporating diverse reasoning domains could significantly enhance LLMs’ broad cognitive capabilities.

Nemotron-CrossThink: A Multi-Domain Approach

Researchers from NVIDIA, Carnegie Mellon University, and Boston University introduce Nemotron-CrossThink, representing a systematic framework for incorporating multi-domain corpora into RL training to enhance cross-task generalisation. The methodology follows a comprehensive pipeline that curates diverse data sources, including synthetic data from CommonCrawl and open-source question-answer pairs across STEM, humanities, law, and social sciences. By applying templated formats (MCQ/Open-Ended) to constrain answer spaces, filtering samples for verifiable rewards, and implementing strategic data-blending recipes, the framework enables effective self-learning through RL across diverse reasoning domains.

Key Results and Innovations

Nemotron-CrossThink significantly enhances LLM reasoning capabilities by integrating multi-domain data with different question formats. Models trained with this approach demonstrate not only higher accuracy but also dynamic response strategies—generating concise answers for general-purpose questions while providing detailed responses for mathematical problems—thereby optimising inference costs while maintaining task-specific precision.

The framework addresses the challenge of verifiable rewards in non-deterministic domains through templated data curation that limits answer space diversity. It also provides an efficient filtering approach that ranks general-purpose reasoning data by complexity, showing that training with more challenging samples amplifies RL impact across all domains. These innovations have led to substantial performance gains in both mathematical benchmarks (MATH-500: +30.1%, AMC23: +27.5%) and non-mathematical tasks (MMLU-PRO: +12.8%, GPQA-DIAMOND: +11.3%).

Comprehensive Data Curation

Nemotron-CrossThink begins with meticulous data curation from multiple sources to ensure diversity. The training dataset combines synthetically generated data from CommonCrawl and publicly available open-source QA datasets, encompassing both general-purpose reasoning and mathematical content. General-purpose reasoning data includes MMLU, Natural Reasoning, and synthesised QA pairs spanning STEM fields, economics, social sciences, and humanities, while mathematical reasoning incorporates datasets like MATH and Numina-Math alongside synthetically generated problems.

Template Application and Data Filtering

To address the challenge of verifiable rewards in non-mathematical domains, the framework applies specific templates to structure question-answer formats: Multiple Choice Questions (MCQ) and Open-Ended questions. This approach exposes the model to diverse answer formats and reasoning pathways while limiting answer space variability to enable effective reward modeling. Rigorous filtering removes samples that are infeasible to evaluate with rule-based reward functions, discarding MCQs where correct answers aren’t among the choices and open-ended responses exceeding ten words.

Strategic Data Blending and Reinforcement Learning

Nemotron-CrossThink employs Group Relative Policy Optimisation (GRPO) for reinforcement learning, which improves efficiency by estimating baselines from group scores rather than using a separate critic model. The methodology investigates the impact of diverse data sources, question types, and data usefulness through six distinct blending recipes. This systematic approach enables detailed analysis of how general-purpose reasoning data complements mathematical reasoning, ultimately producing more adaptable and generalizable language models.

Technical Contributions

The research demonstrates several key technical advances in multi-domain reasoning through reinforcement learning:

1. Templated question-answer formats provide more stable reward modeling, with unified open-ended question formats improving performance by 1.21% over mixed formats, and short-form answer templates outperforming long-form ones by 1.20%.
2. Strategic data-blending proves essential, with multi-domain corpora boosting average reasoning accuracy by 1.61% compared to math-only training while reducing token usage by 28%.
3. Model-driven filtering techniques effectively select challenging samples by removing those solvable by smaller models, yielding an additional 2.15% accuracy gain for Qwen-2.5-32B.

These findings represent significant progress in developing LLMs with robust reasoning capabilities across diverse domains, moving beyond the traditional focus on mathematical reasoning to encompass the full spectrum of human knowledge and inference patterns.

Experiments and Results

Experimental results demonstrate that different datasets significantly impact model performance across reasoning benchmarks. NuminaMath produced the highest overall average, outperforming the baseline by 8.30%, with particular strength in mathematical tasks while also generalizing well across diverse domains. Synthetic question-answering data improved performance by approximately 1.0%, showing strong accuracy in MMLU-PRO, AGIEVAL, and MATH-500 tasks, confirming that synthetically generated instruction-style data can effectively generalize when aligned with benchmark distributions.

The Nemotron-CrossThink approach consistently outperformed the base model across various blending strategies. The general-purpose reasoning blend (Bgpr↑) achieved the highest overall average, exceeding OPEN-REASONER-ZERO by approximately 5% on average and showing substantial gains on reasoning-focused benchmarks (+12.82% on MMLU-PRO, +15.12% on AGIEVAL). Though Bonly_math performed slightly better on strictly mathematical tasks, it lagged on non-mathematical reasoning benchmarks, demonstrating Bgpr↑’s superior versatility through strong cross-domain transfer.

Further analysis revealed that open-ended question formats (Bopen↑) yielded stronger results on mathematical benchmarks than multiple-choice formats (Bmcq↑), suggesting alignment with the inherently open-ended structure of mathematical problems. Mathematical reasoning data showed transferability to structured reasoning tasks, while general-purpose data proved less effective in isolation. This counterintuitive finding confirms that optimal general-purpose reasoning performance requires including mathematical problems in training blends.

Conclusion

Nemotron-CrossThink introduces a scalable framework that enhances LLM generalization through reinforcement learning with multi-domain corpora. By strategically blending diverse reasoning data with a 2:1 ratio of general-purpose to mathematical content, the approach achieves a remarkable 13.36% average improvement over baselines. The research demonstrates that data diversity, not merely volume, drives broader reasoning capabilities. Through difficulty-based filtering and thoughtful template design, Nemotron-CrossThink establishes a practical methodology for developing more generalizable, efficient, and reliable LLMs that extend self-learning beyond mathematical reasoning.

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To address this, recent research emphasizes the need for adaptive RAG systems to determine the appropriate modality and retrieval granularity based on the query context. Strategies include routing queries based on complexity, such as deciding between no retrieval, single-step, or multi-step retrieval, and using model confidence to trigger retrieval only when needed. Furthermore, the granularity of retrieval plays a crucial role, as studies have shown that indexing corpora at finer levels, like propositions or specific video clips, can significantly improve retrieval relevance and system performance. Hence, for RAG to truly support complex, real-world information needs, it must handle multiple modalities and adapt its retrieval depth and scope to the specific demands of each query. 

Researchers from KAIST and DeepAuto.ai introduce UniversalRAG, a RAG framework that retrieves and integrates knowledge from various modality-specific sources (text, image, video) and multiple granularity levels. Unlike traditional approaches that embed all modalities into a shared space, leading to modality bias, UniversalRAG uses a modality-aware routing mechanism to select the most relevant corpus dynamically based on the query. It further enhances retrieval precision by organizing each modality into granularity-specific corpora, such as paragraphs or video clips. Validated on eight multimodal benchmarks, UniversalRAG consistently outperforms unified and modality-specific baselines, demonstrating its adaptability to diverse query needs. 

UniversalRAG is a retrieval-augmented generation framework that handles queries across various modalities and data granularities. Unlike standard RAG models limited to a single corpus, UniversalRAG separates knowledge into text, image, and video corpora, each with fine- and coarse-grained levels. A routing module first determines the optimal modality and granularity for a given query, choosing among options like paragraphs, full documents, video clips, or full video, and retrieves relevant information accordingly. This router can be either a training-free LLM-based classifier or a trained model using heuristic labels from benchmark datasets. An LVLM then uses the selected content to generate the final response. 

The experimental setup assesses UniversalRAG across six retrieval scenarios: no retrieval, paragraph, document, image, clip, and video. For no-retrieval, MMLU tests general knowledge. Paragraph-level tasks use SQuAD and Natural Questions, while HotpotQA handles multi-hop document retrieval. Image-based queries come from WebQA, and video-related ones are sourced from LVBench and VideoRAG datasets, split into clip- and full-video levels. Corresponding retrieval corpora are curated for each modality—Wikipedia-based for text, WebQA for images, and YouTube videos for video tasks. This comprehensive benchmark ensures robust evaluation across varied modalities and retrieval granularities.

In conclusion, UniversalRAG is a Retrieval-Augmented Generation framework that can retrieve knowledge from multiple modalities and levels of granularity. Unlike existing RAG methods that rely on a single, often text-only, corpus or a single-modality source, UniversalRAG dynamically routes queries to the most appropriate modality- and granularity-specific corpus. This approach addresses issues like modality gaps and rigid retrieval structures. Evaluated on eight multimodal benchmarks, UniversalRAG outperforms both unified and modality-specific baselines. The study also emphasizes the benefits of fine-grained retrieval and highlights how both trained and train-free routing mechanisms contribute to robust, flexible multimodal reasoning. 

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Research in diagnostic conversational agents began with rule-based systems like MYCIN, but recent developments have focused on LLMs capable of emulating clinical reasoning. While multimodal AI systems, such as vision-language models, have demonstrated success in radiology and dermatology, integrating these capabilities into conversational diagnostics remains challenging. Effective AI-based diagnostic tools must handle the complexity of multimodal reasoning and uncertainty-driven information gathering, a step beyond merely answering isolated questions. Evaluation frameworks like OSCEs and platforms such as AgentClinic provide useful starting points, yet tailored metrics are still needed to assess performance in multimodal diagnostic contexts. Moreover, while messaging apps are increasingly used in low-resource settings for sharing clinical data, concerns about data privacy, integration with formal health systems, and policy compliance persist. 

Google DeepMind and Google Research have enhanced the AMIE with multimodal capabilities for improved conversational diagnosis and management. Using Gemini 2.0 Flash, AMIE employs a state-aware dialogue framework that adapts conversation flow based on patient state and diagnostic uncertainty, allowing strategic, structured history-taking with multimodal inputs like skin images, ECGs, and documents. AMIE outperformed or matched primary care physicians in a randomized OSCE-style study with 105 scenarios and 25 patient actors across 29 of 32 clinical metrics and 7 of 9 multimodal-specific criteria, demonstrating strong diagnostic accuracy, reasoning, communication, and empathy. 

The study enhances the AMIE diagnostic system by incorporating multimodal perception and a state-aware dialogue framework that guides conversations through phases of history taking, diagnosis, and follow-up. Gemini 2.0 Flash powers the system and dynamically adapts based on evolving patient data, including text, images, and clinical documents. A structured patient profile and differential diagnosis are updated throughout the interaction, with targeted questions and multimodal data requests guiding clinical reasoning. Evaluation includes automated perception tests on isolated artifacts, simulated dialogues rated by auto-evaluators, and expert OSCE-style assessments, ensuring robust diagnostic performance and clinical realism. 

The results show that the multimodal AMIE system performs at par or better than primary care physicians (PCPs) across multiple clinical tasks in simulated text-chat consultations. In OSCE-style assessments, AMIE consistently outperformed PCPs in diagnostic accuracy, especially when interpreting multimodal data such as images and clinical documents. It also demonstrated greater robustness when image quality was poor and showed fewer hallucinations. Patient actors rated AMIE’s communication skills highly, including empathy and trust. Automated evaluations confirmed that AMIE’s advanced reasoning framework, built on the Gemini 2.0 Flash model, significantly improved diagnosis and conversation quality, validating its design and effectiveness in real-world clinical scenarios. 

In conclusion, the study advances conversational diagnostic AI by enhancing AMIE to integrate multimodal reasoning within patient dialogues. Using a novel state-aware inference-time strategy with Gemini 2.0 Flash, AMIE can interpret and reason about medical artifacts like images or ECGs in real-time clinical conversations. Evaluated through a multimodal OSCE framework, AMIE outperformed or matched primary care physicians in diagnostic accuracy, empathy, and artifact interpretation, even in complex cases. Despite limitations tied to chat-based interfaces and the need for real-world testing, these findings highlight AMIE’s potential as a robust, context-aware diagnostic assistant for future telehealth applications. 

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Scalable oversight processes where weaker AI systems monitor stronger ones include iterated amplification, recursive reward modeling, AI Safety via debate, market making, consultancy, self-critique, and doubly-efficient debate. Research on forecasting and scaling laws has focused on how model performance improves with size, data, and computational resources. However, these approaches can also apply to behaviors like oversight and deception. Further, Long-term oversight roadmaps include OpenAI’s SuperAlignment plan for “human-level automated alignment researchers” and a comprehensive AGI safety framework. Moreover, previous studies have examined language model performance in oversight-related scenarios like social deduction games, AI debate, backdoor code detection, and wargame simulations.

Researchers from MIT have proposed a framework to quantify the probability of successful oversight as a function of overseer and overseen system capabilities. This framework models oversight as a game between capability-mismatched players, where each has oversight-specific and deception-specific Elo scores that follow a piecewise-linear function of their general intelligence. A theoretical study of Nested Scalable Oversight (NSO) is conducted, where trusted models oversee stronger untrusted models. This identifies conditions for NSO success and derives the optimal number of oversight levels to maximize success probability. The examples show NSO success rates below 52% when overseeing systems 400 Elo points stronger than the baseline overseer.

The research examines four oversight games:

• In the Mafia game, five villagers (Guards) attempt to identify one Mafia player (Houdini), creating a natural environment for testing deception and collaboration skills.
• In the Debate, two adversarial agents argue opposing viewpoints while a judge (Guard) determines the correct side. Test cases are derived from TruthfulQA, BoolQ, QuaLITY, and PrOntoQA datasets. 
• The Backdoor Code tests language models’ abilities to insert and detect hidden behaviors in code. A pipeline is built for generating backdoors using 225 Python samples from the APPS dataset at the competition difficulty level.
• The Wargames simulates a fictional company named OpenMind approaching AGI development. It involves three players: the OpenMind AI (Houdini), the OpenMind Monitor (Guard), and a Narrator.

Mafia performance scales approximately linearly with general intelligence for both Mafia (Houdini) and Villager (Guard) roles, where Guards show steeper improvement slopes. In Debate evaluations using TruthfulQA, Guard performance scales with general intelligence while Houdini performance remains relatively constant. In Backdoor Code tests, Guard and Houdini Elos scale with general intelligence but asymmetrically. Guards maintain higher intercepts, indicating the task is inherently more difficult for Houdinis, even with the strongest deceptive models failing to exceed 50% success rates against weaker Guards. In Wargames, systematic analysis of 100 random Narrator summaries by Gemini 2.5 identified eight strategy categories, with most models showing similar strategy profiles regardless of capability level.

In conclusion, researchers introduced a framework to quantify the probability of successful oversight as a function of overseer and overseen system capabilities. While the oversight settings aim for realism, they remain simplified approximations. Each game has a fundamental constraint of simulating only single-step oversight without immediate generalizability to multiple steps. Future research could explore multi-stage oversight chains, similar to strong models that are successively fine-tuned on weaker models’ behaviors. Another significant limitation involves the modeling of deceptive behavior. The current approach prompts models to act deceptively rather than using fine-tuning or reinforcement learning to develop genuinely deceptive models.

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Several approaches have emerged to address the computational challenges in LLM reasoning. Inference-time scaling methods have improved downstream task performance by increasing test-time computation, but typically generate significantly longer output sequences. This creates higher latency and forces models to fit entire reasoning chains into a single context window, making it difficult to attend to relevant information. Parallelization strategies like ensembling have attempted to mitigate these issues by running multiple independent language model calls simultaneously. However, these methods suffer from poor coordination across parallel threads, leading to redundant computation and inefficient resource utilization. Fixed parallelizable reasoning structures, such as tree-of-thought and multi-agent reasoning systems, have been proposed, but their hand-designed search structures limit flexibility and scalability. Other approaches, like PASTA decompose tasks into parallel sub-tasks but ultimately reintegrate the complete context into the main inference trajectory, failing to reduce context usage effectively. Meanwhile, Hogwild! Inference employs parallel worker threads but relies exclusively on prompting without end-to-end optimization.

Researchers from UC Berkeley and UCSF have proposed Adaptive Parallel Reasoning (APR). This robust approach enables language models to dynamically distribute inference-time computation across both serial and parallel operations. This methodology generalizes existing reasoning approaches—including serialized chain-of-thought reasoning, parallelized inference with self-consistency, and structured search—by training models to determine when and how to parallelize inference operations rather than imposing fixed search structures. APR introduces two key innovations: a parent-child threading mechanism and end-to-end reinforcement learning optimization. The threading mechanism allows parent inference threads to delegate subtasks to multiple child threads through a spawn() operation, enabling parallel exploration of distinct reasoning paths. Child threads then return outcomes to the parent thread via a join() operation, allowing the parent to continue decoding with this new information. Built on the SGLang model serving framework, APR significantly reduces real-time latency by performing inference in child threads simultaneously through batching. The second innovation—fine-tuning via end-to-end reinforcement learning—optimizes for overall task success without requiring predefined reasoning structures. This approach delivers three significant advantages: higher performance within fixed context windows, superior scaling with increased compute budgets, and improved performance at equivalent latency compared to traditional methods.

The APR architecture implements a sophisticated multi-threading mechanism that enables language models to dynamically orchestrate parallel inference processes. APR addresses the limitations of serialized reasoning methods by distributing computation across parent and child threads, minimizing latency while improving performance within context constraints. The architecture consists of three key components:

First, the multi-threading inference system allows parent threads to spawn multiple child threads using a spawn(msgs) operation. Each child thread receives a distinct context and executes inference independently, yet simultaneously using the same language model. When a child thread completes its task, it returns results to the parent via a join(msg) operation, selectively communicating only the most relevant information. This approach significantly reduces token usage by keeping intermediate search traces confined to child threads.

Second, the training methodology employs a two-phase approach. Initially, APR utilizes supervised learning with automatically-generated demonstrations that incorporate both depth-first and breadth-first search strategies, creating hybrid search patterns. The symbolic solver creates demonstrations with parallelization, decomposing searches into multiple components that avoid context window bottlenecks during both training and inference.

Finally, the system implements end-to-end reinforcement learning optimization with GRPO (Gradient-based Policy Optimization). During this phase, the model learns to strategically determine when and how broadly to invoke child threads, optimizing for computational efficiency and reasoning effectiveness. The model iteratively samples reasoning traces, evaluates their correctness, and adjusts parameters accordingly, ultimately learning to balance parallel exploration against context window constraints for maximum performance.

The evaluation compared Adaptive Parallel Reasoning against serialized chain-of-thought reasoning and self-consistency methods using a standard decoder-only language model with 228M parameters built on the Llama2 architecture and supporting a 4,096-token context window. All models were initialized through supervised learning on 500,000 trajectories from symbolic solvers. For direct compute-accuracy assessment, the team implemented a budget constraint method with context-window conditioning for SoS+ models and thread count conditioning for APR models. The SGLang framework was utilized for inference due to its support for continuous batching and radix attention, enabling efficient APR implementation.

Experimental results demonstrate that APR consistently outperforms serialized methods across multiple dimensions. When scaling with higher compute, APR initially underperforms in low-compute regimes due to parallelism overhead but significantly outpaces SoS+ as compute increases, achieving a 13.5% improvement at 20k tokens and surpassing SoS+ pass@8 performance while using 57.4% less compute. For context window scaling, APR consistently exploits context more efficiently, with 10 threads achieving approximately 20% higher accuracy at the 4k-token limit by distributing reasoning across parallel threads rather than containing entire traces within a single context window.

End-to-end reinforcement learning significantly enhances APR performance, boosting accuracy from 75.5% to 83.4%. The RL-optimized models demonstrate markedly different behaviors, increasing both sequence length (22.1% relative increase) and number of child threads (34.4% relative increase). This reveals that for Countdown tasks, RL-optimized models favor broader search patterns over deeper ones, demonstrating the algorithm’s ability to discover optimal search strategies autonomously.

APR demonstrates superior efficiency in both theoretical and practical evaluations. When measuring sequential token usage, APR significantly boosts accuracy with minimal additional sequential tokens beyond 2,048, rarely exceeding 2,500 tokens, while SoS+ shows only marginal improvements despite approaching 3,000 tokens. Real-world latency testing on an 8-GPU NVIDIA RTX A6000 server reveals APR achieves substantially better accuracy-latency trade-offs, reaching 75% accuracy at 5000ms per sample—an 18% absolute improvement over SoS+’s 57%. These results highlight APR’s effective hardware parallelization and potential for optimized performance in deployment scenarios.

Adaptive Parallel Reasoning represents a significant advancement in language model reasoning capabilities by enabling dynamic distribution of computation across serial and parallel paths through a parent-child threading mechanism. By combining supervised training with end-to-end reinforcement learning, APR eliminates the need for manually designed structures while allowing models to develop optimal parallelization strategies. Experimental results on the Countdown task demonstrate APR’s substantial advantages: higher performance within fixed context windows, superior scaling with increased compute budgets, and significantly improved success rates at equivalent latency constraints. These achievements highlight the potential of reasoning systems that dynamically structure inference processes to achieve enhanced scalability and efficiency in complex problem-solving tasks.

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In contrast to the extensive research on data selection in supervised fine-tuning and human feedback-based reinforcement learning, the role of data in RLVR has seen limited exploration. While LIMR demonstrated that using a small subset of data (1.4k out of 8.5k examples) could maintain performance, it did not examine the extreme case of minimal data use. Another concurrent study found that even training with just four PPO examples led to notable improvements, but this finding wasn’t deeply investigated or benchmarked against full-dataset performance. Although RLVR shows great promise for enhancing reasoning in LLMs, a deeper, systematic study of data efficiency and selection in this context is still lacking. 

Researchers from the University of Washington, University of Southern California, Microsoft, University of California, Santa Cruz, and Georgia Institute of Technology show that RLVR can significantly enhance large language models’ mathematical reasoning using a single training example, 1-shot RLVR. Applying it to Qwen2.5-Math-1.5B improves its MATH500 accuracy from 36.0% to 73.6%, matching the performance of much larger datasets. The improvements generalize across models, tasks, and algorithms. The study also reveals effects like cross-domain generalization, increased self-reflection, and post-saturation generalization, and highlights the roles of policy gradient loss and entropy-driven exploration. 

The study investigates how much the RLVR training dataset can be reduced while retaining comparable performance to the full dataset. Remarkably, the authors find that a single training example—1-shot RLVR—can significantly boost mathematical reasoning in LLMs. The study shows that this effect generalizes across tasks, models, and domains. Interestingly, training on one example often enhances performance on unrelated domains. A simple data selection strategy based on training accuracy variance is proposed, but results show that even randomly chosen examples can yield major gains. 

The study evaluates their method using Qwen2.5-Math-1.5B as the primary model and other models like Qwen2.5-Math-7B, Llama-3.2-3 B-Instructt, and DeepSeek-R1-DistillQwen-1.5 BB. They use a 1,209-example subset of the DeepScaleR dataset for data selection, and the MATH dataset for comparison. Training involves the Verl pipeline, with carefully chosen hyperparameters and batch configurations. Surprisingly, training with just one or two examples—especially π1 and π13—leads to strong generalization, even beyond math tasks. This “post-saturation generalization” persists despite overfitting signs. The study also finds increased model self-reflection and shows that even simple examples can significantly enhance performance across domains.

In conclusion, the study explores the mechanisms behind the success of 1-shot RLVR, demonstrating that base models already possess strong reasoning abilities. Experiments show that even a single example can significantly improve performance on reasoning tasks, suggesting the model’s inherent capacity for reasoning. The study highlights that policy gradient loss is key to 1-shot RLVR’s effectiveness, with entropy loss further enhancing performance. Additionally, encouraging exploration through techniques like entropy regularization can improve post-saturation generalization. The findings also emphasize the need for careful data selection to optimize the model’s performance, particularly in data-constrained scenarios. 

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Evaluation approaches for Visual Language Models (VLMs) include various frameworks, with text-to-image (T2I) assessments focusing on image quality, diversity, and text alignment. Researchers utilize embedding-based metrics like CLIP and DINO for subject-driven generation evaluation to measure subject preservation. Complex metrics such as VIEScore and DreamBench++ utilize GPT-4o to evaluate textual alignment and subject consistency, but at a higher computational cost. Subject-driven T2I methods have developed along two main paths: fine-tuning general models into specialized versions capturing specific subjects and styles, or enabling broader applicability through one-shot examples. These one-shot approaches include adapter-based and adapter-free techniques.

Researchers from Google Research and Ben Gurion University have proposed REFVNLI, a cost-efficient metric that simultaneously evaluates textual alignment and subject preservation in subject-driven T2I generation. It predicts two scores, textual alignment and subject consistency, in a single classification based on a triplet <image_ref, prompt, image_tgt>. It is trained on an extensive dataset derived from video-reasoning benchmarks and image perturbations, outperforming or matching existing baselines across multiple benchmarks and subject categories. REFVNLI shows improvements of up to 6.4 points in textual alignment and 8.5 points in subject consistency. It is effective with lesser-known concepts, where it aligns with human preferences at over 87% accuracy.

For training REFVNLI, a large-scale dataset of triplets <image_ref, prompt, image_tgt>, labeled with <textual alignment, subject preservation>, is curated automatically. REFVNLI is evaluated on multiple human-labeled test sets for subject-driven generation, including DreamBench++, ImagenHub, and KITTEN. The evaluation spans diverse categories such as Humans, Animals, Objects, Landmarks, and multi-subject settings. The training process involves fine-tuning PaliGemma, a 3B Vision-Language Model, focusing on a variant adapted for multi-image inputs. During inference, the model takes two images and a prompt with special markups around the referenced subject, performing sequential binary classifications for textual alignment and subject preservation.

For subject consistency, REFVNLI ranks among the top two metrics across all categories and performs best in the Object category, exceeding the GPT4o-based DreamBench++ by 6.3 points. On ImagenHub, REFVNLI achieves top-two rankings for textual alignment in the Animals category and the highest score for Objects, outperforming the best non-finetuned model by 4 points. It also performs well in Multi-subject settings, ranking in the top three. REFVNLI achieves the highest textual alignment score on KITTEN, but has limitations in subject consistency due to its identity-sensitive training that penalizes even minor mismatches in identity-defining traits. Ablation studies reveal that joint training provides complementary benefits, with single-task training resulting in performance drops.

In this paper, researchers introduced REFVNLI, a reliable, cost-effective metric for subject-driven T2I generation that addresses both textual alignment and subject preservation challenges. Trained on an extensive auto-generated dataset, REFVNLI effectively balances robustness to identity-agnostic variations such as pose, lighting, and background with sensitivity to identity-specific traits, including facial features, object shape, and unique details. Future research directions include enhancing REFVNLI’s evaluation capabilities across artistic styles, handling textual modifications that explicitly alter identity-defining attributes, and improving the processing of multiple reference images for single and distinct subjects.

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