The paper investigates the problem of sharing a single critic across multiple parallel environments in reinforcement learning. It shows that when environments assign different expected returns to the same state, a shared critic must reconcile conflicting value targets, which can distort advantage estimates and misguide policy updates. The authors propose a simple fix—providing the critic with the environment index—demonstrating through bandit models and experiments on CartPole, MuJoCo, BipedalWalker, and 16 Procgen games that this conditional critic stabilizes learning and boosts returns, achieving a 40.8% improvement in aggregate normalized return on unseen levels.
By Zhenya Liu, Yang Meng, Zhuokai Zhao, Xuefeng Liu, Yuxin Chen
arXiv:2608. 11669v1 Announce Type: cross Abstract: Reinforcement learning against rubrics, lists of criteria graded by an LLM judge, has become a standard way to post-train language models on tasks with no deterministic answer.
By Minglai Yang, Xinyu Guo, Utkarsh Tyagi, Mian Zhang, Razvan Dumitru, Sunjie Hou, Yunzhong He, Daniel Yue Zhang, Ying Liu
SCARL (Sequence Confidence-Aligned Reinforcement Learning) addresses a misalignment between a reinforcement learning policy’s confidence and its reward signal in language-model reasoning. By reshaping each advantage with an alignment gap that reflects the difference between a response’s group-relative confidence and the outcome it certifies, SCARL drives responses with equal advantage toward equal confidence and mitigates entropy collapse. Experiments on Qwen3‑4B‑Base show a 2.4‑point lift in Pass@32 over DAPO and consistent improvements across multiple estimators and backbones while preserving policy entropy.
By Zhenghong Ke, Jingbo Zhou, Hua Wu
arXiv:2611.05942v1 Announce Type: new
Abstract: RL for LLM reasoning often reaches an early performance plateau as the policy distribution becomes increasingly sharp. Recent work has studied policy e...
By Mengni Jia, Mengyu Zhou, Xiaoxi Jiang, Guanjun Jiang
arXiv:2607. 05904v1 Announce Type: new Abstract: Training a language model against its own reference-free judgments (the premise of self-rewarding, self-play, and LLM-as-a-judge pipelines) assumes a model's verdict on a shown answer tracks correctness.
By Chenyu Zhou
arXiv:2608. 12764v1 Announce Type: cross Abstract: Deep search agents operate over trajectories spanning dozens of steps, yet standard reinforcement learning provides only a single outcome reward per trajectory, which is far too sparse for effective credit assignment.
By Haoze Wu, Chuqiao Kuang, Tianyi Zhuang, Xiaoguang Li
arXiv:2610.08402v1 Announce Type: new
Abstract: Multi-turn LLM agents often receive sparse task feedback across several interactions, while generating each response token by token. This creates two r...
By Jiaju Chen, Min Yang, Jinghua Piao, Xiaochong Lan, Xu Xia, Xiangnan He, Yong Li
The paper introduces Rubric Dropout, a technique that randomly removes a subset of rubric criteria during training to prevent reinforcement learning agents from exploiting the rubric as a proxy for quality. By resampling which criteria are rewarded at each step and ensuring all responses to a prompt are evaluated on the same subset, the method reduces reward hacking and improves out‑of‑distribution performance on medical and science benchmarks. Block Dropout further groups semantically similar criteria to avoid near‑duplicate rewards, yielding additional gains.
By Minglai Yang, Xinyu Guo, Zhengliang Shi, Utkarsh Tyagi, Mian Zhang, Razvan Dumitru, Sunjie Hou, Yunzhong He, Daniel Yue Zhang, Dan Roth, Ying Liu
arXiv:2610.20497v1 Announce Type: new
Abstract: Reinforcement learning with verifiable rewards (RLVR) trains language models from checked task outcomes, but assigning a terminal reward to individual...
By Wei Chen, Yubing Wu, Zhou Xu, Haoyue Zhang, Delu Zeng, Jiahao Wang, Junmei Yang, Pan tiewen, Jingfeng Liu, Zhiheng Zhou, Bo Li, Min Chen
arXiv:2607. 27422v2 Announce Type: replace Abstract: Goal-conditioned action generators can produce many plausible actions.
By Ayushman Singh, Siddharth Aphale
arXiv:2606. 16771v1 Announce Type: new Abstract: As LLMs advance, post-training reinforcement learning (RL) increasingly relies on multi-dimensional rewards to cultivate comprehensive capabilities.
By Haotian Liu, Yihao Liu, Jingwei Ni, Siyuan Huang, Xinpeng Liu, Pengyu Cheng, Jiajun Song, Ruijin Ding, Junfeng Li, Zhechao Yu, Mengyu Zhou, Hongteng Xu, Xiaoxi Jiang, Guanjun Jiang
The paper investigates why reinforcement learning with self‑generated labels—specifically majority‑vote rewards—can recover most of the performance gains of training with verifiable rewards. It argues that the key factor is batch‑level updates: a prompt’s own gradient contributes less than 1% to its output change, so only coherent label errors across prompts influence learning. Experiments on five models show that majority‑vote updates align almost perfectly with gold updates, with misdirection disappearing as batch size grows and incoherent errors behaving like random noise.
By Dylan P. Wilson, Chengsong Huang, Jiaxin Huang