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AI Research
arXiv CS.AI·Jun 10·5 min read
SocraticPO: Policy Optimization via Interactive Guidance
TL;DR
Reinforcement learning (RL) for large language models often relies on simple rewards, which can lead to poor reasoning. SocraticPO (Socratic Policy Optimization) enhances RL by incorporating Socratic-style guidance during training, allowing models to learn from their mistakes with corrective feedback.
✦ Why It Matters
Engineers can implement SocraticPO to improve the reasoning capabilities of language models in their applications.
Key Takeaways
- Integrate Socratic-style guidance in RL training.
- Use reward decay to discourage reliance on teacher assistance.
- Evaluate models on diverse reasoning benchmarks.
- Leverage black-box teacher models for enhanced feedback.
Full Summary
Reinforcement learning (RL) typically uses scalar rewards, like binary correctness, to guide large language models, which can result in models learning shortcuts rather than robust reasoning. SocraticPO (Socratic Policy Optimization) was developed to address this by integrating interactive, natural-language guidance during the training process.
In this framework, a student model first attempts to answer a question independently; if incorrect, a teacher model provides concise corrective feedback before the student continues. Importantly, correct answers following teacher intervention receive reduced rewards, discouraging reliance on assistance.
Evaluated on undergraduate-level scientific reasoning benchmarks from SciKnowEval, SocraticPO outperformed strong RL and self-distillation methods. Ablation studies confirmed that both the targeted guidance and the reward decay mechanism are essential for effective learning.
These findings suggest that incorporating interactive guidance can significantly enhance the training of language models.