exploration-exploitation-rlvr

Investigate exploration-exploitation trade-offs in reinforcement learning with verifiable rewards through theoretical analysis and empirical validation. Derive explicit clipping bias bounds, establish policy-entropy shift formulation, and introduce reward-misalignment framework. Show policy entropy and performance lack direct causal relationships.

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