Nash Learning from Human Feedback (2312.00886v4)

Abstract: Reinforcement learning from human feedback (RLHF) has emerged as the main paradigm for aligning LLMs with human preferences. Typically, RLHF involves the initial step of learning a reward model from human feedback, often expressed as preferences between pairs of text generations produced by a pre-trained LLM. Subsequently, the LLM's policy is fine-tuned by optimizing it to maximize the reward model through a reinforcement learning algorithm. However, an inherent limitation of current reward models is their inability to fully represent the richness of human preferences and their dependency on the sampling distribution. In this study, we introduce an alternative pipeline for the fine-tuning of LLMs using pairwise human feedback. Our approach entails the initial learning of a preference model, which is conditioned on two inputs given a prompt, followed by the pursuit of a policy that consistently generates responses preferred over those generated by any competing policy, thus defining the Nash equilibrium of this preference model. We term this approach Nash learning from human feedback (NLHF). In the context of a tabular policy representation, we present a novel algorithmic solution, Nash-MD, founded on the principles of mirror descent. This algorithm produces a sequence of policies, with the last iteration converging to the regularized Nash equilibrium. Additionally, we explore parametric representations of policies and introduce gradient descent algorithms for deep-learning architectures. To demonstrate the effectiveness of our approach, we present experimental results involving the fine-tuning of a LLM for a text summarization task. We believe NLHF offers a compelling avenue for preference learning and policy optimization with the potential of advancing the field of aligning LLMs with human preferences.

An Analysis of Nash Learning from Human Feedback in LLMs

The paper "Nash Learning from Human Feedback" presents a nuanced exploration of aligning LLMs with human preferences through a novel approach leveraging game-theoretic principles. This paper introduces an alternative to the traditional Reinforcement Learning from Human Feedback (RLHF) paradigm by focusing on direct preference modeling and computing Nash equilibria. The authors argue for the use of preference models as a more expressive and effective mechanism than reward models for capturing human preferences in the context of LLM fine-tuning.

Key Contributions

1. Preference Model vs. Reward Model: The paper emphasizes the limitations of traditional reward models, often based on the Bradley-Terry model or Elo ratings, suggesting that they fail to capture the richness and complexity of human preferences. The authors propose leveraging preference models that handle non-transitive preferences and are distributionally robust, making them less sensitive to the policy used for data collection.
2. Nash Equilibrium as an Objective: The core proposition is to shift from optimizing a reward model to optimizing the Nash equilibrium of a preference model. The Nash equilibrium is argued to inherently align better with the diversity of human preferences by encapsulating the concept of mutual best responses in a game-theoretic context.
3. Algorithmic Innovation with Nash-MD: The paper introduces the Nash-MD algorithm, a novel variant of mirror descent designed to converge to the Nash equilibrium of the regularized preference model. This algorithm performs a mirror descent step targeting a mixture policy that balances between the initial and current policies, providing a scalable and effective mechanism for policy optimization without the need for extensive memory to store past policies.
4. Experimental Analysis: The paper presents comprehensive experimentation on text summarization tasks to demonstrate the efficacy of the proposed Nash learning approach. The results indicate that leveraging a preference model and Nash equilibrium provides improved alignment with human preferences compared to RLHF baselines.

Implications and Speculation on AI Developments

The implications of this research are substantial both in theoretical advancements and practical applications. Theoretically, the use of Nash equilibrium in machine learning contexts offers a promising direction for more robust and interpretable model training paradigms, particularly in environments where preferences are diverse and possibly conflicting. Practically, this approach can potentially improve the way AI systems, especially conversational and assistive agents, interact with users by better understanding and aligning with human intentions.

This work could catalyze further exploration into incorporating game-theoretic concepts into AI training and model optimization. Future developments may include exploring different game-theoretic solution concepts or extending Nash equilibrium frameworks to multi-agent systems where interactions become even more complex.

Conclusion

In conclusion, "Nash Learning from Human Feedback" represents a significant stride toward refining LLMs and their alignment with human expectations. By advocating for a preference-centric approach and employing Nash equilibria, this research provides a compelling alternative to the conventional RLHF framework. It opens the door to advancing upon the overarching goal of more naturally integrated AI systems capable of decision-making that resonates with human values and social norms. Future investigations will likely delve into scalability, the integration of more comprehensive feedback mechanisms, and the expansion of these concepts to broader AI domains.