Reinforcement learning has substantially improved large language model (LLM) agents in verifiable domains, but remains difficult to apply to open-ended agent tasks, where solutions are diverse and reliable scalar rewards are hard to obtain. Recent pairwise evaluation methods alleviate reward discrimination collapse by...
Qiang Zhang, Rui-Xue Ding, Fanrui Zhang et al.· 1 citation
Direct preference alignment methods are widely used to align large language models (LLMs) with human preferences because of their computational and memory efficiency. However, likelihood displacement motivates alternative ways to extract information from preference pairs with small likelihood margins. In this paper, we...
Peter Chen, Xi Chen, Wo-Tao Yin et al.· 0 citations
ARISE-RL, a novel full-cycle self-evolution framework that couples a task/rubric Generator and a reasoning Solver through rubric-mediated co-evolution, is proposed and ECR-Bench, an expert-calibrated rubric benchmark suite covering single-tool deep research and multi-tool travel planning is presented.
Fanrui Zhang, Rui-Xue Ding, Qiang Zhang et al.· 0 citations
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