The paper investigates how to apply Reinforcement Learning with Verifiable Rewards (RLVR) to large language models across multiple domains. It compares two training paradigms—mixed multi-task RLVR and separate RLVR followed by model merging—using tasks such as math, coding, science, instruction following, and agent. Experiments show that RLVR across domains causes minimal interference and that reasoning-intensive domains can synergize, with insights drawn from information constraints, prediction behavior, and self-verification.
By Haoqing Wang, Xiang Long, Ziheng Li, Yilong Xu, Tingguang Li, Yehui Tang
arXiv:2610.20174v1 Announce Type: new
Abstract: Reinforcement Learning on Mixed-domain preference data (Mix-RL) has become the mainstream approach for training general-purpose Generative Reward Model...
By Qinglin Su, Licheng Pan, Zhen Fang, Jiakang Wang, Qingnan Ren, Yunsheng Lu, Yan Gao, Zhixuan Chu
The paper introduces POISE, a reinforcement learning algorithm that uses a model’s internal states as a value estimator to reduce variance in reinforcement learning with verifiable rewards (RLVR). By employing a lightweight probe that reads internal signals during the forward pass, POISE predicts baselines online and uses a cross‑rollout construction to keep gradients unbiased. Experiments on Qwen3‑4B and OLMo3‑7B‑Instruct‑DPO across six domains show POISE outperforms existing RLVR baselines, offering more stable training and a value model that generalizes across tasks and scales with the policy.
By Yunho Choi, Jongwon Lim, Woojin Ahn, Minjae Oh, Jeonghoon Shim, Yohan Jo
arXiv:2510. 21978v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has delivered impressive gains in mathematical and multimodal reasoning and has become a standard post-training paradigm for contemporary language and vision-language models.
By Hoang Phan, Xianjun Yang, Yuanshun Yao, Jingyu Zhang, Shengjie Bi, Xiaocheng Tang, Madian Khabsa, Lijuan Liu, Deren Lei
arXiv:2608.24696v1 Announce Type: cross
Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) and on-policy distillation (OPD) have become two widely adopted paradigms for post-training lar...
By Wenze Lin, Jiale Zhao, Xitai Jiang, Songde Rao, Yining Li, Shenzhi Wang, Bingxiang He, Gao Huang
arXiv:2606. 25178v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has been extended from single-domain training to multi-domain reasoning suites spanning mathematics, programming, and science.
By Yongjin Yang, Jiarui Liu, Yinghui He, Lechen Zhang, Bernhard Sch\"olkopf, Zhijing Jin
ResMerge is a new framework for merging large language models trained via reinforcement learning. It separates each model’s task vector into a leading spectral head and a residual component, finding that both parts contain valuable behavior knowledge but behave differently during merging. The method builds a stable residual backbone using Spherical Residual Consensus Adaptation and then adds a lightweight head correction module that activates only when experts agree, leading to better preservation of expert capabilities compared to existing merging baselines.
By Yandu Sun, Zhiyan Hou, Hongyan An, Weizhen Wang, Haokai Ma, Yuheng Jia, Junfeng Fang, Haiyun Guo, Jinqiao Wang
The paper introduces Skill-Decoupled On-Policy Expert Distillation (SCOPED), a method that trains separate experts for each capability and extracts explicit skills via reward-guided reflection. During on‑policy distillation, these skills supervise the experts but are not exposed to the unified student, encouraging the student to learn procedural knowledge internally. Experiments on eight multimodal benchmarks show SCOPED consistently outperforms both the base model and mixed‑capability reinforcement learning, highlighting the benefits of skill decoupling for integrating independently acquired capabilities.
By Yuqi Xiong, Chunyi Peng, Zhipeng Xu, Qing Yang, Zhenghao Liu, Zulong Chen, Yu Gu, Ge Yu
arXiv:2610.02359v1 Announce Type: cross
Abstract: Reinforcement learning from verifiable rewards (RLVR) usually optimizes answer correctness, yet useful language-model behavior also requires high-qua...
By Doseok Jang, Jon Ander Campos, Youran Qi
arXiv:2610.32492v1 Announce Type: cross
Abstract: Large language models serve applications with diverse inference budgets, but training and maintaining separate models for each budget incurs substant...
By Yudong Zhang, Wei Ding, Kaili Zheng, Yuchen Ding, Zhenyu Zhang
arXiv:2607. 19450v1 Announce Type: cross Abstract: Large-scale online reinforcement learning (RL) is the predominant means of eliciting advanced abilities including long-term reasoning and agentic tool use in large language models (LLMs).
By Yunjie Chen, Xiaoxin Chen, Fang Wang
The paper introduces Policy-Compatible Reinforcement Learning (PICO), a method that addresses the learning cliff in reinforcement learning with verifiable rewards (RLVR) by performing distribution-constrained joint decoding. PICO guides token-level decoding with expert data while preserving the model’s token probabilities, and uses a dynamic top‑k mechanism to keep sampling within a finite divergence budget. The resulting trajectories are verified and policy‑compatible, and are jointly optimized with on‑policy rollouts using a mixed‑behavior objective that mitigates distribution mismatch via importance sampling, leading to consistent performance gains over standard RLVR and prior off‑policy baselines on diverse reasoning datasets and agentic benchmarks.
By Zehao Wang, Hanzhu Chen, Runquan Gui, Zhe Wang, Lin Yang