2024 TrainingLanguageModelstoSelfCor
- (Kumar et al., 2024) ⇒ Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei M Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal Behbahani, and Aleksandra Faust. (2024). “Training Language Models to Self-Correct via Reinforcement Learning.”
Subject Headings: Intrinsic Self-Correction, Self-Correction via Reinforcement Learning, Multi-Turn Reinforcement Learning,Reward Shaping, On-Policy Learning, Distribution Shift, Policy Initialization.
Notes
Thank you for the guidance. I'll revise the bullet points with more appropriate and unambiguous wiki links:
- Intrinsic Self-Correction: The paper addresses the challenge of teaching Large Language Models (LLMs) to correct their own mistakes without external feedback. This is a crucial skill for improving model performance and reliability.
- Distribution Shift: The authors highlight the problem of distribution mismatch between training data and the model's own responses. This concept is important in machine learning, where models often perform poorly on data that differs from their training distribution.
- Supervised Fine-Tuning Limitations: The paper demonstrates that traditional Supervised Fine-Tuning (SFT) methods can amplify a model's bias towards making only minor edits or no changes at all. This insight shows the limitations of standard training approaches.
- Multi-Turn Reinforcement Learning: The proposed Self-Correction via Reinforcement Learning (SCoRe) method, outlined in the paper, uses a multi-turn approach, allowing the model to learn from its own attempts at correction. This is a more sophisticated training strategy than single-turn methods.
- Reward Shaping: The authors use reward shaping in their method to incentivize the model to learn a self-correction strategy. This concept involves modifying the reward function to guide the learning process more effectively.
- Policy Initialization: The paper emphasizes the importance of careful policy initialization to prevent collapse during Reinforcement Learning. This underscores the significance of starting conditions in machine learning algorithms.
- On-Policy vs. Off-Policy Learning: The research shows the benefits of on-policy learning (training on the model's own distribution) over off-policy methods. This distinction is crucial in Reinforcement Learning.
- Generalization in Meta-Learning: The authors draw parallels to the memorization challenge in meta-learning, discussing the difficulty of learning generalizable strategies versus memorizing specific solutions.
- Inference-Time Compute Scaling: The paper explores how self-correction can be combined with inference-time strategies like self-consistency to improve performance. This highlights the interplay between training methods and inference techniques.
- Ablation Studies: The researchers conduct thorough ablation studies to understand the impact of different components in their method. This demonstrates the importance of systematic experimentation in machine learning research.
- Evaluation Metrics: The paper introduces several metrics for measuring self-correction performance, such as Δ(t1, t2) and Δi→c(t1, t2). Understanding these metrics is crucial for assessing the effectiveness of self-correction methods.
Cited By
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Abstract
Self-correction is a highly desirable capability of large language models (LLMs), yet it has consistently been found to be largely ineffective in modern LLMs. Existing approaches for training self-correction either require multiple models or rely on a more capable model or other forms of supervision. To this end, we develop a multi-turn online reinforcement learning (RL) approach, SCoRe, that significantly improves an LLM's self-correction ability using entirely self-generated data. To build SCoRe, we first show that variants of supervised fine-tuning (SFT) on offline model-generated correction traces are insufficient for instilling self-correction behavior. In particular, we observe that training via SFT either suffers from a distribution mismatch between the training data and the model's own responses or implicitly prefers only a certain mode of correction behavior that is often not effective at test time. SCoRe addresses these challenges by training under the model's own distribution of self-generated correction traces and using appropriate regularization to steer the learning process into learning a self-correction strategy that is effective at test time as opposed to simply fitting high-reward responses for a given prompt. This regularization prescribes running a first phase of RL on a base model to generate a policy initialization that is less susceptible to collapse and then using a reward bonus to amplify self-correction during training. When applied to Gemini 1.0 Pro and 1.5 Flash models, we find that SCoRe achieves state-of-the-art self-correction performance, improving the base models' self-correction by 15.6% and 9.1% respectively on the MATH and HumanEval benchmarks.
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