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H^2SD: Hybrid Hindsight Self-Distillation

5 upvotes · 21 JUL 2026 · Qiye Cai, Yichuan Ma, Linyang Li et al.

This paper introduces H^2SD, a hybrid hindsight self-distillation framework for reinforcement learning with verifiable rewards, which combines the strengths of different methods to improve large language models' reasoning capabilities. Practitioners may care about this work because it addresses limitations of existing methods and shows promising results on challenging reasoning benchmarks.

reinforcement learningverifiable rewardsself-distillationhybrid methodstoken-level credit assignment