Entropy-Regularized Token-Level Policy Optimization for Large Language Models

Temel Kavramlar

The author introduces Entropy-Regularized Token-level Policy Optimization (ETPO) as a method to optimize Large Language Models at the token level, addressing challenges in RL fine-tuning. ETPO decomposes actions into tokens and provides fine-grained credit assignment, improving performance.

Özet

The paper presents ETPO, an entropy-augmented RL method tailored for optimizing LLMs at the token level. It addresses challenges in assigning credit based on action-level reward signals and explores a simulated environment for data science code generation tasks. ETPO shows effective performance improvement over baselines like PPO inherited from RLHF.
Large language models (LLMs) have shown promise in interactive decision-making tasks across various domains. Reinforcement learning (RL) offers a dynamic approach for LLMs to refine their performance in task-specific environments. However, challenges arise due to misalignment in learning objectives between language modeling and RL optimization granularity.
To bridge these gaps, the authors propose ETPO, which leverages entropy-regularized reinforcement learning to train LLM agents for verbal sequential decision-making tasks. By decomposing optimization from action space to token space, ETPO provides fine-grained credit assignment and improves model performance.
In experiments modeling data science code generation tasks as interactive decision-making environments, ETPO outperforms baselines like Reflection and PPO-KL. The method demonstrates stability and convergence in training processes while maintaining fundamental language modeling capabilities.
Further research could explore integrating ETPO with self-rewarding or hindsight relabeling techniques to address challenges with quantitative reward functions. The study highlights the potential of leveraging hallucination in LLMs with RL training for creativity and innovation.

İstatistikler

Environment Step=1, Reward=0.7463
Environment Step=16, Reward=0.8716
Environment Step=224, Reward=0.8968
Environment Step=496, Reward=0.9289

Alıntılar

"In this paper, we take several steps to bridge these gaps between RL and language modeling."
"Our experiments confirm the effectiveness of ETPO within a simulated environment that models data science code generation."

Önemli Bilgiler Şuradan Elde Edildi

Entropy-Regularized Token-Level Policy Optimization for Large Language Models

by Muning Wen,C... : arxiv.org 03-06-2024

https://arxiv.org/pdf/2402.06700.pdf

Daha Derin Sorular

How can integrating ETPO with self-rewarding techniques enhance its effectiveness?

Integrating ETPO with self-rewarding techniques can enhance its effectiveness by providing a mechanism for the model to prompt itself and provide rewards during training. This approach allows the model to receive feedback based on its own actions, leading to more targeted and personalized learning. By incorporating self-rewarding techniques, ETPO can adapt and improve based on its own performance, making the training process more efficient and effective.

What are the implications of leveraging hallucination in LLMs with RL training for creativity?

Leveraging hallucination in LLMs with RL training can have significant implications for creativity. Hallucinations, which refer to unexpected or non-standard outputs generated by language models, can serve as a source of innovation and creativity when filtered through an exploration-exploitation framework in RL. By allowing models to explore unconventional paths that may lead to high rewards, hallucinations become a driving force for discovering new behaviors or capabilities within the model. This approach encourages serendipitous discoveries and promotes creative thinking in language generation tasks.

How can the findings of this study be applied to other domains beyond machine learning?

The findings of this study have broader applications beyond machine learning:

Natural Language Processing (NLP): The decomposition technique used in ETPO could be applied to NLP tasks such as text summarization or dialogue generation.

Robotics: The concept of fine-grained credit assignment from token-level policy updates could be beneficial in reinforcement learning algorithms for robotic control tasks.

Healthcare: Applying entropy-regularized reinforcement learning methods like ETPO could optimize treatment plans or medical decision-making processes.

Finance: Utilizing self-rewarding techniques inspired by this study could improve trading strategies or risk management systems.

By adapting these methodologies from machine learning into various domains, researchers and practitioners can enhance decision-making processes, optimize workflows, and drive innovation across different industries.