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Benefits and Pitfalls of Reinforcement Learning for Language Model Planning: A Theoretical Perspective
arXiv – CS AI|Siwei Wang, Yifei Shen, Haoran Sun, Shi Feng, Shang-Hua Teng, Li Dong, Yaru Hao, Wei Chen||1 views
🤖AI Summary
New research provides theoretical analysis of reinforcement learning's impact on Large Language Model planning capabilities, revealing that RL improves generalization through exploration while supervised fine-tuning may create spurious solutions. The study shows Q-learning maintains output diversity better than policy gradient methods, with findings validated on real-world planning benchmarks.
Key Takeaways
- →Supervised fine-tuning may introduce co-occurrence-based spurious solutions in LLM planning tasks.
- →Reinforcement learning achieves correct planning primarily through exploration, enabling better generalization.
- →Policy gradient methods suffer from diversity collapse where output variety decreases during training.
- →Q-learning provides advantages through off-policy learning and diversity preservation at convergence.
- →Careful reward design is necessary to prevent Q-value bias in Q-learning applications.
#reinforcement-learning#large-language-models#ai-research#machine-learning#llm-planning#q-learning#policy-gradient#ai-theory
Read Original →via arXiv – CS AI
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