3 papers
cs.LG2026
Scaling Reward Modeling without Human Supervision
Jingxuan Fan, Yueying Li, Zhenting Qi +4
Learning from feedback is an instrumental process for advancing the capabilities and safety of frontier models, yet its effectiveness is often constrained by cost and scalability.…
cs.CL2026
Test-time Recursive Thinking: Self-Improvement without External Feedback
Yufan Zhuang, Chandan Singh, Liyuan Liu +5
Modern Large Language Models (LLMs) have shown rapid improvements in reasoning capabilities, driven largely by reinforcement learning (RL) with verifiable rewards. Here, we ask whe…
cs.LG2025
FlowRL: Matching Reward Distributions for LLM Reasoning
Xuekai Zhu, Daixuan Cheng, Dinghuai Zhang +20
We propose FlowRL: matching the full reward distribution via flow balancing instead of maximizing rewards in large language model (LLM) reinforcement learning (RL). Recent advanced…