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Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning
arXiv – CS AI|Yihe Deng, I-Hung Hsu, Jun Yan, Zifeng Wang, Rujun Han, Gufeng Zhang, Yanfei Chen, Wei Wang, Tomas Pfister, Chen-Yu Lee||6 views
🤖AI Summary
Researchers propose Supervised Reinforcement Learning (SRL), a new training framework that helps small-scale language models solve complex multi-step reasoning problems by generating internal reasoning monologues and providing step-wise rewards. SRL outperforms traditional Supervised Fine-Tuning and Reinforcement Learning approaches, enabling smaller models to tackle previously unlearnable problems.
Key Takeaways
- →SRL addresses limitations of existing training methods by reformulating problem-solving as a sequence of logical actions with internal reasoning.
- →The framework provides smoother rewards based on similarity to expert actions, offering richer learning signals even when all attempts are incorrect.
- →Small models can now learn challenging problems that were previously impossible with traditional SFT or RLVR methods.
- →Combining SRL initialization with RLVR refinement yields the strongest overall performance results.
- →The framework generalizes effectively beyond reasoning benchmarks to practical software engineering tasks.
#reinforcement-learning#language-models#machine-learning#reasoning#training-methods#small-models#srl#llm-training#ai-research#model-optimization
Read Original →via arXiv – CS AI
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