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arXiv Computation and Language

Consolidating RLVR Capabilities Across Domains: A Deep Dive into Fusion Paradigms

The paper investigates three fusion paradigms—Merge, Mix RL, and multi‑teacher on‑policy distillation (MOPD)—for consolidating reinforcement learning with verifiable rewards (RLVR) across multiple domains. Experiments across model scales and a multi‑domain benchmark show that while overall performance differences are small, significant gaps can appear on specific tasks, and each method exhibits distinct training dynamics and constraints. Practical guidelines are offered: Merge for cheap fusion when experts exist, Mix RL for unified training with adjustable domain mixtures, and MOPD when preserving domain‑specific gains is paramount.

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Sep 10

To Mix or To Merge: Toward Multi-Domain Reinforcement Learning for Large Language Models

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
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arXiv AI
Jun 19

Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models

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
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arXiv Computation and Language
Aug 27

ResMerge: Residual-based Spectral Merging of Large Language Models

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
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arXiv AI
Jun 18

Sparsity Curse: Understanding RLVR Model Parameter Space from Model Merging

arXiv:2606. 18521v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Reward (RLVR) has emerged as a powerful post-training paradigm that surpasses Supervised Fine-Tuning (SFT) in eliciting reasoning intelligence and resisting catastrophic forgetting.

By Chenrui Wu, Zexi Li, Jiajun Bu, Jiangchuan Liu, Haishuai Wang
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arXiv Machine Learning
Jul 2

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training

arXiv:2607. 01232v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a central component of post-training large language models (LLMs), yet little is understood about how RL adaptation is distributed across transformer layers.

By Zijian Zhang, Rizhen Hu, Athanasios Glentis, Dawei Li, Chung-Yiu Yau, Hongzhou Lin, Mingyi Hong
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arXiv Machine Learning
Sep 4

DE-Venus: A Data-Efficient RLVR Framework for Large Language Models

DE‑Venus is a unified, data‑efficient framework for reinforcement learning with verifiable rewards (RLVR) tailored to large language models. It structures the RLVR lifecycle into three modules—Active Data Selection, Weak Supervision Construction, and Training‑Time Supervision Refinement—allowing method‑specific decisions to be expressed as dataset transitions or online transformations while maintaining distributed execution contracts. Experiments on public benchmarks and three business scenarios show that DE‑Venus can preserve or improve model quality using only 10% of labels or 13% of relevant data, and can cut convergence steps by 63%–75% in selected business configurations.

By Shenzhi Yang, Guangcheng Zhu, Kai Tang, Zhengqing Zang, Xing Zheng, Haobo Wang, Yingfan Ma, Bowen Song, Bo Han, Bo An, Lei Feng, Weiqiang Wang, Junbo Zhao, Gang Chen
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arXiv Computation and Language
Aug 25

Every Coin Has Two Sides: On the Dual Nature of Generalization in On-Policy Distillation of Large Language Models

arXiv:2608.16647v2 Announce Type: replace Abstract: On-policy distillation (OPD) transfers teacher capabilities by supervising trajectories sampled from the student's own policy, yet its generalizati...

By Zhaoyi Li, Deyang Kong, Yuan Wei, Evan Yang, Ranran Shen, Mahardika Krisna Ihsani, Ming Yang, Wei Zhang, Chuan Hao, Jian Yang, Ran Tao, Bryan Dai, Shikun Zhang, Wei Ye, Ying Wei, Defu Lian
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