Advancements in Reinforcement Learning from Human Feedback and instruction fine-tuning are enhancing Language Model’s (LLM) capabilities, aligning them more closely with human preferences and making complex behaviors more accessible. Expert Iteration is found to outperform other methods, bridging the performance gap between pre-trained and supervised fine-tuned LLMs. Research indicates the importance of RL fine-tuning and prompts for improving LLM reasoning capabilities.
Enhancing Language Model Reasoning with Expert Iteration: Bridging the Gap Through Reinforcement Learning
Introduction
The capabilities of Language Model Reasoning (LLMs) are rapidly advancing, particularly in mathematics, science, and coding tasks. Advancements in Reinforcement Learning from Human Feedback (RLHF) and instruction fine-tuning are aligning LLMs more closely with human preferences, making complex behaviors more accessible through instruction prompting. Innovative prompting strategies like Chain-of-Thought or Tree-of-Thoughts further augment LLM reasoning. Integrating RL into LLM reasoning represents a natural progression, leveraging interactive problem-solving dynamics for enhanced performance.
Research Findings
Researchers have investigated various RL algorithms’ effectiveness in enhancing the reasoning capabilities of LLMs, with Expert Iteration (EI) consistently outperforming other methods. The study highlights the significance of RL fine-tuning in bridging the performance gap between pre-trained and supervised fine-tuned LLMs. Combining LLMs with planning algorithms and tools further enhances their reasoning capabilities.
Practical Applications
Experiments on reasoning tasks for LLMs showcase the effectiveness of RL fine-tuning, particularly EI, in improving model performance and generalization, providing better generalization and diversity in solution paths than static fine-tuning. Further advancements in prompting techniques and model exploration are crucial for improving Language Model reasoning capabilities.
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