AI RESEARCH
LambdaPO: A Lambda Style Policy Optimization for Reasoning Language Models
arXiv CS.CL
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ArXi:2605.19416v1 Announce Type: new Group Relative Policy Optimization(GRPO) has become a cornerstone of modern reinforcement learning alignment, prized for its efficacy in foregoing an explicit value-critic by leveraging reward normalization across sampled trajectory cohorts. However, the method's reliance on a monolithic statistical baseline, such as the group mean, collapses the relational topology of the trajectory space into a single scalar, thereby erasing the fine-grained preference information essential for navigating complex, rank-sensitive reward landscapes.