The paper introduces Reasoning State Propagation (RSP), a method that models each reasoning prefix with a binary validity state and learns transitions between successive states. RSP predicts break and repair probabilities to connect intermediate reasoning states to the final outcome, enabling outcome supervision to guide learning of earlier steps. Experiments on reasoning search, response selection, and reinforcement learning show RSP consistently outperforms existing Process Reward Models, achieving notable gains over Qwen2.5-Math-PRM.
arXiv:2605. 02395v2 Announce Type: replace Abstract: Process reward models (PRMs) rely on high-quality process supervision data, yet existing construction methods often provide limited control over error location, error type, and trajectory consistency.
arXiv:2606. 11209v1 Announce Type: cross Abstract: Visual question answering increasingly requires multi-step reasoning.
arXiv:2604. 09482v2 Announce Type: replace Abstract: Reasoning in knowledge-intensive domains remains challenging as intermediate steps are often not locally verifiable: unlike math or code, evaluating step correctness may require synthesizing clues across large external knowledge sources.
The paper introduces the Implicit Prefix-Value Reward Model (IPVRM), which learns the probability of eventual correctness for each prefix directly from outcome labels, thereby aligning training targets with inference-time step signals via temporal-difference differences. IPVRM improves step-verification F1 on ProcessBench. Additionally, the authors propose Distribution-Level RL (DistRL), a policy optimization method that applies TD advantages to both sampled and high-probability tokens, offering dense counterfactual updates without extra rollouts, and show that DistRL consistently enhances downstream reasoning when combined with IPVRM.
arXiv:2609.36641v1 Announce Type: cross Abstract: Process reward models (PRMs) have become a key component for LLMs, as their step-level feedback supports both post-training and test-time reasoning....
arXiv:2606. 09078v1 Announce Type: new Abstract: Process Reward Models (PRMs) improve credit assignment for reasoning by providing step-level feedback.
arXiv:2512.03244v2 Announce Type: replace-cross Abstract: Training process reward models (PRMs) requires step-level correctness labels, obtained either through expensive human annotation or by relyin...
arXiv:2604. 23333v2 Announce Type: replace Abstract: Scaling test-time computation with reinforcement learning (RL) has emerged as a reliable path to improve large language models (LLM) reasoning ability.
Cliff is a reward‑shaping method for reinforcement learning with verifiable rewards that identifies the first mistake in a language model’s reasoning process using an off‑the‑shelf teacher. By splitting each rollout into a correct prefix and an incorrect suffix, Cliff assigns token‑level advantages—positive for correct tokens and negative for the rest—providing fine‑grained supervision. Across 12 scenarios, Cliff outperforms on‑policy distillation by 15% and standard GRPO by 7%, even when the teacher is only modestly capable.
arXiv:2609.37119v1 Announce Type: cross Abstract: Recent approaches to reinforcement learning (RL) post-training for large language models increasingly remove the critic to reduce training instabilit...
arXiv:2609.22746v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently been introduced into traffic signal control (TSC) as decision agents due to their strengths in human-readabl...
arXiv:2605. 12519v2 Announce Type: replace-cross Abstract: Training language models to produce both correct answers and sound reasoning remains an open challenge.