Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents

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reinforcement-learning post-training llm-agents progress-advantage reward-models step-level-scoring

Summary

This paper introduces 'progress advantage', an implicit advantage function derived from reinforcement learning post-training that enables effective step-level scoring for LLM agents without requiring dedicated reward model training. It outperforms confidence-based baselines and trained reward models across multiple benchmarks and model families.

Process reward models enable fine-grained, step-level evaluation of LLMs, yet building them for agentic settings remains prohibitively difficult: long-horizon interactions, irreversible actions, and stochastic environment feedback make both human annotation and Monte Carlo estimation infeasible at scale. In this work, we show that reinforcement learning (RL) post-training already provides the ingredients for effective step-level scoring, eliminating the need for dedicated reward model training altogether. Concretely, we derive an implicit advantage under a general stochastic Markov decision process, which we term progress advantage -- log-probability ratio between the RL-trained policy and its reference policy exactly recovers the optimal advantage function. This formulation makes the resulting signal annotation-free, domain-agnostic, and available as a byproduct of the standard RL post-training pipeline. We validate the effectiveness of the progress advantage across three different applications: test-time scaling, uncertainty quantification, and failure attribution on five benchmarks and four model families. Across all settings, it consistently outperforms confidence-based baselines and, despite requiring no task-specific training, surpasses dedicated trained reward models. We complement these results with deeper analyses on characteristics of progress advantage, offering practical guidance for adoption in real-world agentic systems.
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Cached at: 06/26/26, 10:09 PM

Paper page - Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents

Source: https://huggingface.co/papers/2606.26080

Abstract

Reinforcement learning post-training enables effective step-level scoring for language models without requiring dedicated reward model training by deriving an implicit advantage function called progress advantage.

Processreward modelsenable fine-grained, step-level evaluation of LLMs, yet building them foragentic settingsremains prohibitively difficult: long-horizon interactions, irreversible actions, and stochastic environment feedback make both human annotation and Monte Carlo estimation infeasible at scale. In this work, we show thatreinforcement learning(RL) post-training already provides the ingredients for effective step-level scoring, eliminating the need for dedicated reward model training altogether. Concretely, we derive an implicit advantage under a general stochasticMarkov decision process, which we termprogress advantage--log-probability ratiobetween the RL-trained policy and its reference policy exactly recovers the optimaladvantage function. This formulation makes the resulting signal annotation-free, domain-agnostic, and available as a byproduct of the standard RL post-training pipeline. We validate the effectiveness of theprogress advantageacross three different applications:test-time scaling,uncertainty quantification, andfailure attributionon five benchmarks and four model families. Across all settings, it consistently outperforms confidence-based baselines and, despite requiring no task-specific training, surpasses dedicated trainedreward models. We complement these results with deeper analyses on characteristics ofprogress advantage, offering practical guidance for adoption in real-world agentic systems.

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