11h ago
Hidden States Know Where Reasoning Diverges: Credit Assignment via Span-Level Wasserstein Distance
★★★★★
significance 3/5
The paper introduces SHEAR, a method to improve reinforcement learning by using hidden-state distributions to identify where reasoning diverges. It uses Wasserstein distance to provide finer-grained credit assignment in models like GRPO, allowing for better token-level optimization using only outcome-level labels.
Why it matters
Granular credit assignment via hidden-state analysis promises to bridge the gap between outcome-only rewards and efficient token-level optimization.
Tags
#reinforcement learning #grpo #hidden states #credit assignment #wasserstein distanceRelated coverage
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