Outcome-Grounded Advantage Reshaping for Fine-Grained Credit Assignment in Mathematical Reasoning
arXiv:2601.07408v2 Announce Type: replace-cross Abstract: Group Relative Policy Optimization (GRPO) has emerged as a promising critic-free reinforcement learning paradigm for reasoning tasks. However, standard GRPO employs a coarse-grained credit assignment mechanism that propagates group-level rewards uniformly to to every token in a sequence, neglecting the varying contribution of individual reasoning steps. We address this limitation by introducing Outcome-grounded Advantage Reshaping (OAR), a fine-grained credit assignment mechanism that redistributes advantages based on how much each toke
The continuous advancements in AI research, particularly in reinforcement learning and complex reasoning tasks, necessitate more sophisticated credit assignment mechanisms for improved performance.
This development allows AI systems to perform mathematical and other reasoning tasks with greater accuracy and efficiency by pinpointing individual contributions within a sequence of operations.
AI models will be able to learn from complex problem-solving much more effectively, leading to more robust and capable autonomous systems.
- · AI researchers
- · Reinforcement learning platforms
- · Developers of AI agents
- · Industries requiring complex automated reasoning
- · AI systems relying on coarse-grained credit assignment
Improved performance of AI systems in complex, multi-step reasoning tasks.
Accelerated development of more reliable and intelligent AI agents capable of handling intricate logical problems.
Broadened applications of AI in scientific discovery, engineering design, and other fields demanding high-fidelity reasoning.
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Read at arXiv cs.LG