Reason, Reward, Refine: Step-Level Errors Corrections with Structured Feedback for Physics Reasoning in Small Language Models
arXiv:2607.05199v1 Announce Type: new Abstract: Physics reasoning fails structurally in small language models: an error at any step propagates forward, corrupting every inference that follows. Limited domain knowledge, hallucination under multi-step derivation, and distributional sensitivity compound this failure. We propose a step-level reward framework that identifies the first reasoning error, generates targeted structured feedback, and trains the model to revise its solution via policy gradient with KL regularization, without exposing it to ground truth solutions as generation targets. Unl
The proliferation of advanced neural networks enables more sophisticated approaches to AI self-correction, which is a critical missing piece for broader deployment.
Improving the reasoning capabilities and error correction of small language models expands their practical application, especially in domains requiring precision like scientific inquiry.
This advancement changes how reliably small language models can be used for multi-step reasoning tasks, reducing the human oversight required for complex problem-solving.
- · AI developers
- · Physics researchers
- · Education technology
- · Companies adopting small language models
- · Manual error correction services
- · Companies reliant on large, unoptimized models
Small language models become significantly more useful for complex, multi-step reasoning tasks.
This improved reliability accelerates the adoption of AI agents in roles demanding logical deduction and iterative refinement.
The democratization of advanced problem-solving capabilities could lead to new scientific discoveries and innovations in previously intractable fields.
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Read at arXiv cs.AI