QVal: Cheaply Evaluating Dense Supervision Signals for Long-Horizon LLM Agents
Evolving story · 1 updatesQVal: Efficient Evaluation of Dense Supervision for LLM AgentsTimeline →Research introduces QVal, a method to cheaply evaluate dense supervision signals for long-horizon LLM agents by scoring intermediate actions, addressing the high cost of traditional downstream performance evaluations.
LLM agents increasingly act over long horizons, where a single trajectory can contain hundreds or thousands of actions. In these settings, outcome-only rewards provide too sparse guidance, failing to inform the model about the goodness of intermediate actions. Dense supervision methods aim to solve this problem by scoring intermediate steps, from intrinsic confidence to self-distillation and embedding similarities. However, it is common practice to evaluate them by measuring the downstream performance of a training pipeline that integrates them. This is expensive, conflates supervision quality
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