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This arXiv preprint proposes "progress advantage," a step-level scoring signal for LLM agents derived from the log-probability ratio between an RL-trained policy and its reference model, which the authors claim recovers the optimal advantage function without training a separate reward model. The reported gains are on test-time scaling, uncertainty quantification, and failure attribution across five benchmarks and four model families, but these are authors' results and not yet independently replicated. For practitioners, the key question is whether the method holds up in real long-horizon agent deployments, where the paper's claims about domain-agnostic, annotation-free signals still need broader validation.
Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents
arXiv:2606.26080v2 Announce Type: replace Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.26080 [cs.LG]
(or arXiv:2606.26080v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.26080 Focus to learn more arXiv-issued DOI via DataCite
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