arXiv:2606.24064v1 Announce Type: new Abstract: Distilling reasoning capabilities from strong to weak language models typically involves imitating specific solution trajectories, effectively transferring what to answer rather than how to reason. This trajectory-level imitation encourages memorization of instance-specific steps rather than acquisition of transferable problem-solving skills, limiting generalization to novel problems. We propose Strategy-Guided Policy Optimization (SGPO), which replaces instance-level trajectory imitation with reusable strategy distillation. SGPO extracts structu
The proliferation of Large Language Models (LLMs) and the demand for more robust, generalized reasoning capabilities are driving innovation in how these models learn and are optimized.
This research suggests a fundamental improvement in LLM training paradigms, moving beyond mere imitation to cultivate deeper, transferable reasoning skills, which is crucial for advanced AI applications.
The focus shifts from 'what to answer' to 'how to reason', potentially leading to LLMs that can generalize better to novel problems rather than just memorizing specific solutions.
- · AI researchers and developers
- · Companies building agentic AI systems
- · Sectors requiring complex problem-solving AI
- · Models relying solely on trajectory imitation
- · Applications demanding high generalization with current imitation techniques
Improved performance and reliability of AI models in complex reasoning tasks.
Accelerated development of more autonomous and capable AI agents across various industries.
Reduced computational costs and smaller model sizes for equivalent or superior reasoning capabilities, democratizing advanced AI.
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Read at arXiv cs.AI