AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
Hybrid Reinforcement
HERO (Hybrid Ensemble Reward Optimization) is a reinforcement learning framework that combines binary verifier feedback with continuous reward-model signals to improve LLM reasoning. By using stratified normalization and variance-aware weighting, HERO balances correctness and nuance, outperforming verifier-only and RM-only methods on diverse math reasoning benchmarks and enhancing performance on both verifiable and ambiguous tasks.
Webscale-RL
Webscale-RL introduces a scalable data pipeline that transforms web-scale pretraining text into over 1.2M diverse, verifiable QA pairs for reinforcement learning across 9+ domains. Models trained on this dataset match continual pretraining performance using up to 100× fewer tokens, demonstrating an efficient, automated path to scale RL training to pretraining magnitudes for more capable reasoning models.
The Era of Real-World Human Interaction
This work presents a post-training recipe that learns directly from real user conversations instead of static annotator labels. RLHI combines user-guided rewrites (using follow-ups as corrections) with persona-based rewards (ranking sampled candidates via a persona-conditioned reward model). Trained on WildChat conversations, it shows strong improvements in personalization, instruction following, and even transfers to reasoning tasks.
Language Models that Think, Chat Better
A simple recipe, RL with Model-rewarded Thinking, makes small open models “plan first, answer second” on regular chat prompts and trains them with online RL against a preference reward. On Llama-3.1-8B and Qwen-2.5-7B, this consistently beats standard RLHF on chat, creative writing, and general knowledge, with the best 8B model topping some frontier systems on WildBench and AlpacaEval2.
ACE-RL
A reinforcement-learning framework that replaces coarse, preference-pair rewards with instruction-specific, verifiable checklists. ACE-RL turns each long-form task into a set of explicit and implicit constraints, scores a model’s output by how well it satisfies them, and mixes this with a length-control reward during GRPO training. The result is stronger, more controllable long-form writing across domains and styles.
AggLM
AggLM introduces reinforcement learning to train LLMs in aggregating multiple candidate solutions, moving beyond majority voting and reward model ranking. It achieves higher accuracy, recovers minority-correct answers, generalizes across models, and uses fewer tokens than traditional aggregation methods.
A Survey of RL for Large Reasoning Models
This survey reviews how reinforcement learning is driving advances in large reasoning models (LRMs), enabling stronger performance on complex tasks like math and coding. It highlights scaling challenges in computation, algorithms, data, and infrastructure, while mapping future directions toward Artificial Superintelligence (ASI).
Adaptive LLM Routing
A routing framework that learns online which model to call for each query while honoring a spend limit. It treats routing as a contextual bandit, initializes with human preference data, and adds an online cost policy that allocates budget across queries.
Full-Stack Fine-Tuning for the Q Programming Language
Presents an open-source blueprint for adapting large language models to niche programming domains, with Q (used in quantitative finance) as the test case. The team builds a benchmark, curates data, and trains Qwen-2.5 models with pretraining, supervised fine-tuning, and reinforcement learning. Their largest model surpasses Claude Opus-4 by nearly 30% on Q-LeetCode tasks, and even the smallest model beats GPT-4.1.
A Deep Dive into RL for LLM Reasoning
This paper reviews and rigorously re-evaluates reinforcement learning techniques for LLM reasoning, addressing inconsistencies caused by varied setups and unclear guidelines. It offers a unified open-source framework, practical selection guidelines, and shows that a minimalist two-technique combo with vanilla PPO can outperform methods like GRPO and DAPO.
Geometric-Mean Policy Optimization
Introduces a stabilized alternative to Group Relative Policy Optimization (GRPO), which is widely used to improve reasoning capabilities in large language models via reinforcement learning. GRPO optimizes the arithmetic mean of token-level rewards but suffers from training instability due to extreme importance sampling ratios. GMPO addresses this by instead maximizing the geometric mean of token-level rewards, leading to more stable updates.
Group Sequence Policy Optimization
This paper introduces GSPO, a new RL algorithm designed to improve the training of large language models, particularly under high compute and long-sequence regimes. Unlike GRPO, which applies token-level importance weights, GSPO performs optimization entirely at the sequence level, aligning the unit of reward with the unit of optimization to resolve instability and inefficiency in large-scale RL training.
One Token to Fool LLM-as-a-Judge
Investigates the surprising fragility of LLM-based reward models used in Reinforcement Learning with Verifiable Rewards (RLVR). The authors find that inserting superficial, semantically empty tokens, like “Thought process:”, “Solution”, or even just a colon “:”, can consistently trick models into giving false positive rewards, regardless of the actual correctness of the response.
Scaling up RL
This paper investigates how prolonged RL can enhance reasoning abilities in small language models across diverse domains. Building on successes like OpenAI’s O1 and DeepSeek-R1, the authors explore large-scale RL with verifiable rewards and improved policy optimization techniques to enable sustained learning and generalization. They introduce the Nemotron-Research-Reasoning-Qwen-1.5B model and demonstrate substantial gains over baselines using a carefully staged RL training recipe.
Machine Bullshit
This paper introduces the concept of machine bullshit, extending Harry Frankfurt’s definition, discourse made with indifference to truth, to LLMs. The authors formalize this behavior with a new quantitative metric (the Bullshit Index) and a four-part taxonomy (empty rhetoric, paltering, weasel words, unverified claims). Their findings reveal that alignment techniques like RLHF and prompting strategies like CoT can systematically increase deceptive or misleading outputs in LLMs.
Q-Chunking
Q-chunking is a reinforcement learning approach that uses action chunking to improve offline-to-online learning in long-horizon, sparse-reward tasks. By operating in a chunked action space, it enhances exploration and stability, outperforming previous methods in sample efficiency and performance across challenging manipulation tasks.
Bridging Offline and Online Reinforcement Learning for LLMs
Investigates the effectiveness of different RL strategies for finetuning LLMs, systematically bridging the gap between offline and online methods. Key findings:
Diffusion Steering via RL
This paper introduces Diffusion Steering via Reinforcement Learning (DSRL), a method for adapting pretrained diffusion policies by learning in their latent-noise space instead of finetuning model weights. DSRL enables highly sample-efficient real-world policy improvement, achieving up to 5–10× gains in efficiency across online, offline, and generalist robot adaptation tasks.
Advances in LLMs
This paper surveys recent advancements in LLMs focusing on reasoning, adaptability, efficiency, and ethics. It highlights techniques like CoT prompting, Instruction Tuning, RLHF, and multimodal learning, while also addressing challenges like bias, computational cost, and interpretability.
Self-Adapting Language Models
It proposes a novel framework that enables LLMs to adapt themselves through reinforcement learning by generating their own fine-tuning data and update directives, referred to as “self-edits.” This approach allows models to autonomously optimize their learning process, without relying on separate adaptation modules or human supervision. Key highlights:
ComfyUI-R1
Introduces ComfyUI-R1, a 7B-parameter large reasoning model fine-tuned for automatic workflow generation in the ComfyUI ecosystem. Built on Qwen2.5-Coder and trained through a two-stage pipeline (supervised chain-of-thought reasoning followed by reinforcement learning), ComfyUI-R1 significantly outperforms prior state-of-the-art approaches that rely on commercial models like GPT-4o and Claude 3.5. Key highlights:
Magistral
Mistral introduces Magistral, its first reasoning-focused LLM line, alongside a custom RL training stack that enables pure reinforcement learning from scratch. In contrast to prior approaches that rely on distillation from teacher models, Magistral trains directly using online RL with text-only data and custom reward shaping. The work yields two open models: Magistral Medium (based on Mistral Medium 3) and the open-sourced Magistral Small (24B), which is bootstrapped via SFT on Medium’s outputs, followed by RL. Key insights:
LLamaRL
LlamaRL is a fully-distributed, asynchronous reinforcement learning framework designed for efficient large-scale LLM training (8B to 405B+ models). It achieves up to 10.7× speedup over DeepSpeed-Chat by combining co-located model offloading, asynchronous off-policy training (AIPO), and fast GPU-native weight sync (DDMA), while maintaining model quality across tasks like math reasoning.
RewardBench 2
RewardBench 2 is a new multi-skill benchmark for evaluating reward models with more challenging human prompts and stronger correlation to downstream performance. It highlights gaps in the current reward model's effectiveness and aims to support more rigorous evaluation, showing existing models score ~20 points lower than their predecessor.