Improving Language Model Reasoning with Expert Iteration: Closing the Gap Using Reinforcement Learning

The capabilities of LLMs are advancing rapidly, as evidenced by their performance on various benchmarks in math, science, and coding tasks. At the same time, advances in reinforcement learning from human feedback (RLHF) and instruction tuning are aligning LLMs more closely with human preferences. This progress improves the apparent skills of LLMs, making complex behaviors more accessible through instructional prompts. Innovative motivation strategies such as the thought chain or thought tree further enhance LLM reasoning. Building on the successes in RL techniques observed in gaming environments, the integration of RL into LLM reasoning represents a natural progression, leveraging the interactive dynamics of problem solving to improve performance.

Researchers from Meta, the Georgia Institute of technology, StabilityAI, and UC Berkeley have investigated the effectiveness of various RL algorithms in improving the reasoning capabilities of LLMs across various reward schemes, model sizes, and initializations. Expert iteration (EI) consistently outperforms other methods and shows competitive sample efficiency. The performance of EI approaches that of more complex algorithms such as proximal policy optimization (PPO), and even requires fewer samples for convergence. The study highlights the importance of RL adjustment in closing the performance gap between previously trained and supervised LLMs. Exploration emerges as a critical factor affecting the effectiveness of RL fit for LLMs, with implications for RL from human feedback and the future of LLM fit.

Several studies show the increasing skill of LLMs in tackling complex reasoning tasks, supported by advances such as CoT and Tree of Thought techniques. These methods allow LLMs to postpone final answers by generating intermediate calculations. Combining LLM with algorithms and planning tools further enhances your reasoning abilities. RLHF is a prominent method for tuning LLMs, while expert iteration algorithms show comparable performance. Despite extensive research in RL to improve LLM, an understanding of the most impactful factors still needs to be discovered.

Researchers approach reasoning tasks for LLMs as RL problems, examining the performance and sampling complexity of various RL algorithms for fine-tuning LLMs. The study analyzes EI, PPO and RL with conditional return (RCRL). Each algorithm aims to maximize the expected future performance of a student policy on a given task. The study details PPO, EI, and RCRL methodologies, including exploration strategies, training procedures, and reward mechanisms. The researchers also present results of experiments carried out with these algorithms in reasoning tasks, showing their effectiveness in improving LLM performance.

Experiments on GSM8K and SVAMP datasets evaluate various models using different metrics. Supervised fine-tuning (SFT) data is initially used, followed by experiments without SFT data. EI outperforms other methods and shows significant improvement over the baseline. EI models perform better than PPO models despite more training. The results indicate that RL fine-tuning, particularly EI, provides better generalization and diversity in solution paths than static SFT fine-tuning. Larger models engage in more diverse exploration, which affects model performance during training. These findings shed light on the effectiveness of RL tuning in improving model performance and generalization.

In conclusion, the study findings indicate that EI outperforms other RL algorithms in reasoning tasks. EI and PPO converge rapidly without supervised adjustment, benefiting little from additional guidance or denser rewards. RL fine-tuning improves single- and multi-step accuracy by taking advantage of dynamic synthetic data generation. The study highlights the importance of pre-trained models to enable exploration and suggests limitations in current exploration strategies. Further advances in stimulation techniques and model exploration are crucial to improve language model reasoning capabilities.

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