Theoretical Learnability and Algorithms for Nash Learning from Human Feedback Under General KL-Regularized Preference
Introduction to Nash Learning from Human Feedback
Nash Learning from Human Feedback (NLHF) is a paradigm in machine learning that seeks to align LLM (LM) outputs with human preferences without the direct availability of a reward function. This framework conceptualizes the alignment process as a game between two competing LLMs, aiming to identify a policy that generates responses preferred over any alternative while closely adhering to the initial model. By defining the objective as reaching a Nash Equilibrium within a KL-regularized preference model, NLHF distinguishes itself by its generalizability and applicability to real-world complex preference patterns that extend beyond the capabilities of traditional reward-based systems.
Theoretical Foundations and Algorithmic Developments
The core of this paper lies in providing theoretical insights into the learnability of NLHF, marked by the introduction of algorithms for both offline and batch online settings. For offline learning, relying on pre-collected datasets, the paper introduces two algorithms that employ principles of pessimism under suitable coverage conditions to ensure efficiency. The algorithms' design focuses on achieving an ε-approximate Nash Equilibrium, emphasizing the practical import of these theoretical advancements in enhancing model alignment with human preferences.
In transitioning to the online learning setup, where models iteratively refine based on new feedback, the paper delineates a sample-efficient batch learning algorithm. This algorithm upholds the principle of optimism and introduces a non-symmetric training structure to circumvent the challenges posed by the significant training costs of LLMs. This theoretical model foresees an ε-approximate Nash Equilibrium after a calculable number of updates, offering a structured pathway to continual model improvement in interaction with human feedback.
Empirical Validation and Future Implications
Although primarily theoretical, the proposed algorithms' finite-sample guarantees provide a basis for future empirical validation. The insights regarding the coverage condition and the efficacy of the pessimism principle in offline settings, along with the sample efficiency of the proposed online learning algorithm, offer clear directions for applying these theoretical foundations in real-world scenarios, particularly in refining LLMs through iterative human feedback.
Concluding Remarks
This paper makes significant strides in grounding NLHF in rigorous theoretical learnability studies, bridging the gap with traditional reinforcement learning theory. By navigating the complexities of modeling human preferences without reliance on direct reward signals, the research pushes the frontier of aligning LLMs with nuanced human values and preferences. The introduction of theoretically sound algorithms for offline and online learning underlines the potential of reward-model-free learning in capturing and adhering to human judgements, paving the way for future developments in the field of generative AI and LLMs.