Latest Reinforcement Learning Research Papers

Research on learning through interaction, reward optimization, policy learning, and decision-making AI systems.

9 Papers

Showing 9 of 9 papers

KAGE-Bench: Fast Known-Axis Visual Generalization Evaluation for Reinforcement Learning

Egor Cherepanov, Daniil Zelezetsky, Alexey K. Kovalev +1 more

Pixel-based reinforcement learning agents often fail under purely visual distribution shift even when latent dynamics and rewards are unchanged, but existing benchmarks entangle multiple sources of shift and hinder systematic analysis. We introduce KAGE-Env, a JAX-native 2D platformer that factorize...

pixel-based reinforcement learningvisual distribution shiftlatent dynamicsreward functionJAX-native+5 more

Jan 20, 20268

Behavior Knowledge Merge in Reinforced Agentic Models

Xiangchi Yuan, Dachuan Shi, Chunhui Zhang +4 more

Reinforcement learning (RL) is central to post-training, particularly for agentic models that require specialized reasoning behaviors. In this setting, model merging offers a practical mechanism for integrating multiple RL-trained agents from different tasks into a single generalist model. However, ...

reinforcement learningmodel mergingagentic modelstask vectorssupervised fine-tuning+5 more

Jan 20, 202622

DPWriter: Reinforcement Learning with Diverse Planning Branching for Creative Writing

Qian Cao, Yahui Liu, Wei Bi +6 more

Reinforcement learning (RL)-based enhancement of large language models (LLMs) often leads to reduced output diversity, undermining their utility in open-ended tasks like creative writing. Current methods lack explicit mechanisms for guiding diverse exploration and instead prioritize optimization eff...

Jan 14, 20262

Collaborative Multi-Agent Test-Time Reinforcement Learning for Reasoning

Zhiyuan Hu, Yunhai Hu, Juncheng Liu +9 more

Multi-agent systems have evolved into practical LLM-driven collaborators for many applications, gaining robustness from diversity and cross-checking. However, multi-agent RL (MARL) training is resource-intensive and unstable: co-adapting teammates induce non-stationarity, and rewards are often spars...

Jan 14, 202676

Your Group-Relative Advantage Is Biased

Fengkai Yang, Zherui Chen, Xiaohan Wang +10 more

Reinforcement Learning from Verifier Rewards (RLVR) has emerged as a widely used approach for post-training large language models on reasoning tasks, with group-based methods such as GRPO and its variants gaining broad adoption. These methods rely on group-relative advantage estimation to avoid lear...

Reinforcement Learning from Verifier Rewardsgroup-based methodsGRPOadvantage estimationbias correction+4 more

Jan 13, 2026128

No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning

Zhicong Li, Lingjie Jiang, Yulan Hu +7 more

Critique-guided reinforcement learning (RL) has emerged as a powerful paradigm for training LLM agents by augmenting sparse outcome rewards with natural-language feedback. However, current methods often rely on static or offline critic models, which fail to adapt as the policy evolves. In on-policy ...

Jan 11, 20263

LSRIF: Logic-Structured Reinforcement Learning for Instruction Following

Qingyu Ren, Qianyu He, Jingwen Chang +6 more

Instruction-following is critical for large language models, but real-world instructions often contain logical structures such as sequential dependencies and conditional branching. Existing methods typically construct datasets with parallel constraints and optimize average rewards, ignoring logical ...

instruction-followinglogical structuressequential dependenciesconditional branchingLSRInstruct+7 more

Jan 10, 202610

ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking

Qiang Zhang, Boli Chen, Fanrui Zhang +14 more

Reinforcement learning has substantially improved the performance of LLM agents on tasks with verifiable outcomes, but it still struggles on open-ended agent tasks with vast solution spaces (e.g., complex travel planning). Due to the absence of objective ground-truth for these tasks, current RL algo...

Jan 10, 202637

SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning

Caijun Xu, Changyi Xiao, Zhongyuan Peng +2 more

Reinforcement learning (RL) offers a principled way to enhance the reasoning capabilities of large language models, yet its effectiveness hinges on training signals that remain informative as models evolve. In practice, RL progress often slows when task difficulty becomes poorly aligned with model c...

Jan 8, 20262

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