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Source note2 min read

QVal: Cheaply Evaluating Dense Supervision Signals for Long-Horizon LLM Agents

Source

arXiv

arxiv.org/abs/2606.32034v1

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Plain-Language Brief

The paper introduces QVal, a training-free testbed for evaluating dense supervision signals in long-horizon reinforcement learning tasks. QVal measures the alignment of a method's score with the Q-values of a strong reference policy, allowing comparison between different methods without confounding factors from training engineering. The study compares 21 dense supervision methods across four environments and seven families, using six open-weight model backbones. Results show that simple prompting baselines outperform many recent dense supervision methods, and performance tends to cluster by methodological family.

What the Document Says

  • The paper presents QVal, a testbed for evaluating dense supervision signals in long-horizon reinforcement learning tasks.
  • QVal measures the alignment of a method's score with the Q-values of a strong reference policy.
  • 21 dense supervision methods were compared across four environments and seven families using six open-weight model backbones.
  • Results show that simple prompting baselines outperform many recent dense supervision methods.

Key Concepts Mentioned

  • Long-horizon reinforcement learning
  • Dense supervision signals
  • QVal testbed
  • Q-values
  • Reference policy
  • Methodological families
  • Model backbones

Claims Made by the Document

  • Simple prompting baselines outperform many recent dense supervision methods.
  • Performance tends to cluster by methodological family.

Evidence Used

  • Results from the study

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

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Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

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 with training engineering confounders, and renders different methodological families requiring distinct training setups incomparable. As a result, dense supervision methods are rarely benchmarked on common ground. We introduce QVal, a training-free testbed for directly evaluating dense supervision signals. Given a state-action pair, QVal measures how well a method's score is Q-aligned: whether it orders actions according to the Q-values of a strong reference-policy. This lets us compare signals before any training run and separate signal quality from other engineering choices. We instantiate QVal as QVal-v1.0, benchmarking 21 dense supervision methods across four diverse environments and seven methodological families, with over 1.2K evaluation experiments across six open-weight model backbones. We find that simple prompting baselines consistently outperform recent dense supervision methods from the literature, and that performance clusters strongly by family. These findings hold across model sizes, environments, and observation modalities. QVal is designed to be easily extensible to new environments and methods, enabling researchers to iterate on dense supervision methods before any training run.