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Task-specific training, not just model scale, unlocks effective relative policy difference transfer in distillation, and the reference policy is key to isolating residual capabilities.
Shifting the focus from static state transitions to dynamic, agent-centric feedback could revolutionize how we train and evolve intelligent agents.
Forget static memory—MemHarness reconstructs past experiences to fit the present context, dramatically boosting decision-making performance in LLM agents.
A lightweight proxy model can efficiently discover high-reward behaviors, enabling significant performance boosts in stronger LLMs without the computational burden of traditional methods.