Discovering Process-Outcome Credit in Multi-Step LLM Reasoning

• URL: http://arxiv.org/abs/2602.01034v1
• Date: Sun, 01 Feb 2026 05:44:09 GMT
• Title: Discovering Process-Outcome Credit in Multi-Step LLM Reasoning
• Authors: Xiangwei Wang, Wei Wang, Ken Chen, Nanduni Nimalsiri, Saman Halgamuge,
• Abstract summary: Reinforcement Learning (RL) serves as a potent paradigm for enhancing reasoning capabilities in Large Language Models (LLMs)<n>We propose a novel framework designed to provide continuous reward signals.<n>Our model exhibits superior out-of-distribution robustness, demonstrating promising zero-shot transfer capabilities to unseen and challenging reasoning tasks.
• Score: 3.584086358722852
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement Learning (RL) serves as a potent paradigm for enhancing reasoning capabilities in Large Language Models (LLMs), yet standard outcome-based approaches often suffer from reward sparsity and inefficient credit assignment. In this paper, we propose a novel framework designed to provide continuous reward signals, which introduces a Step-wise Marginal Information Gain (MIG) mechanism that quantifies the intrinsic value of reasoning steps against a Monotonic Historical Watermark, effectively filtering out training noise. To ensure disentangled credit distribution, we implement a Decoupled Masking Strategy, applying process-oriented rewards specifically to the chain-of-thought (CoT) and outcome-oriented rewards to the full completion. Additionally, we incorporate a Dual-Gated SFT objective to stabilize training with high-quality structural and factual signals. Extensive experiments across textual and multi-modal benchmarks (e.g., MATH, Super-CLEVR) demonstrate that our approach consistently outperforms baselines such as GRPO in both sample efficiency and final accuracy. Furthermore, our model exhibits superior out-of-distribution robustness, demonstrating promising zero-shot transfer capabilities to unseen and challenging reasoning tasks.

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