Train at the Moving Edge: Rollout-Efficient RL for Large Reasoning Models
Jiahao Wu · Ning Lu · Shengcai Liu · Kun Wang · Yanting Yang · Baijiong Lin · Chen J Zhang · Qing Li · Ke Tang
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Jiahao Wu · Ning Lu · Shengcai Liu · Kun Wang · Yanting Yang · Baijiong Lin · Chen J Zhang · Qing Li · Ke Tang
Zeguan Xiao · Yun Chen · Jian Yang · Guanhua Chen · Ke Tang
Direct Alignment Algorithms (DAAs), such as Direct Preference Optimization (DPO) and Simple Preference Optimization (SimPO), have emerged as efficient alternatives to Reinforcement Learning from Human Feedback (RLHF) algorithms for aligning large language models (LLMs) with human preferences. How...
Hengle Jiang · Ke Tang
Large Language Model agents deployed in complex environments frequently encounter a conflict between maximizing goal achievement and adhering to safety constraints. This paper identifies a new concept called Agentic Pressure, which characterizes the endogenous tension emerging when compliant exec...