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Supervised Fine-Tuning as Inverse Reinforcement Learning (2403.12017v1)

Published 18 Mar 2024 in cs.LG, cs.AI, and cs.CL

Abstract: The prevailing approach to aligning LLMs typically relies on human or AI feedback and assumes access to specific types of preference datasets. In our work, we question the efficacy of such datasets and explore various scenarios where alignment with expert demonstrations proves more realistic. We build a sequential decision-making framework to formulate the problem of aligning LLMs using demonstration datasets. Drawing insights from inverse reinforcement learning and imitation learning, we introduce various approaches for divergence minimization in the LLM alignment tasks. Our analysis highlights the mass-covering and mode-seeking behaviors of these different approaches. Inclusively, we examine the pros and cons of the classical supervised fine-tuning method, elaborating on scenarios where different methods shine.

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Supervised Fine-Tuning as Inverse Reinforcement Learning: A Deep Dive into LLM Alignment Techniques

Introduction to LLM Alignment Techniques

LLMs are subject to continuous research efforts aimed at enhancing their alignment with human intention and domain-specific accuracy. Traditional alignment techniques have leveraged diverse arrays of methodologies including supervised learning, preference modeling, and contrastive learning. However, Hao Sun's work shifts focus onto leveraging demonstration datasets in LLM alignment, introducing a robust framework that posits supervised fine-tuning within the field of Inverse Reinforcement Learning (IRL). This approach explores the sequential decision-making process, exploring how LLMs can benefit from expert demonstrations over preference-based learning fabricated from human or AI feedback.

Understanding the Theoretical Foundations

Sun's work thrives on the foundational grounds of Markov Decision Processes (MDPs), Online and Offline RL, along with nuances of Behavior Cloning (BC) and Imitation Learning (IL). A critical insight is formulated by distinguishing the auto-regressive nature of LLMs as a sequential decision-making challenge. This pivot allows for the conceptualization of LLM alignment tasks in terms of distribution matching via forward KL divergence, which substantiates the inclination towards supervised fine-tuning practices (SFT) that inherently adopt a mass-covering approach in demonstration-based alignment.

Divergence Minimization in LLM Alignment

Importantly, the paper argues the efficiency and effectiveness of utilizing Rear KL divergence and Jensen-Shannon divergence for potentially fostering mode-seeking behaviors within LLM alignments. These divergences are explored within the context of trajectory distribution and state-action occupancy measures, providing a mathematically rigorous framework to address the alignment challenges. The articulation includes comparing the conventional SFT objectives to the propositions of minimizing these divergences, offering a theoretical basis to reassess alignment methodologies.

Practical Implications and Future Directions

The implications of conceptualizing supervised fine-tuning as an IRL problem are profound, stretching from theoretical elucidations to practical algorithmic developments. This conceptual framework allows for a broader exploration of alignment strategies beyond preference data reliance, paving the way for a deeper understanding of LLMs' learning mechanisms. Importantly, the exploration of alternative divergences opens new avenues for developing more effective and nuanced alignment strategies that could lead to enhanced performance and adaptability of LLMs in varied real-world scenarios.

Conclusions

In sum, Hao Sun's exploration of supervised fine-tuning through the lens of IRL introduces a compelling perspective on aligning LLMs using demonstration datasets. By formalizing the alignment task as a sequential decision-making problem and leveraging insights from IRL, the paper lays down a comprehensive framework that broadens the scope of research in LLM alignment. Moving forward, this work sets a solid foundation for future investigations into sophisticated alignment methodologies that fundamentally harness the power of expert demonstrations, potentially leading to advancements in creating LLMs that are more aligned with human intentions and capable of generating responses with heightened reliability and accuracy.

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Authors (1)
  1. Hao Sun (383 papers)
Citations (2)
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