Transfer Q Star: Principled Decoding for LLM Alignment (2405.20495v1)

Published 30 May 2024 in cs.CL and cs.LG

Abstract: Aligning foundation models is essential for their safe and trustworthy deployment. However, traditional fine-tuning methods are computationally intensive and require updating billions of model parameters. A promising alternative, alignment via decoding, adjusts the response distribution directly without model updates to maximize a target reward $r$, thus providing a lightweight and adaptable framework for alignment. However, principled decoding methods rely on oracle access to an optimal Q-function ($Q^*$), which is often unavailable in practice. Hence, prior SoTA methods either approximate this $Q^*$ using $Q^{{\pi_{\texttt{sft}}}$} (derived from the reference $\texttt{SFT}$ model) or rely on short-term rewards, resulting in sub-optimal decoding performance. In this work, we propose Transfer $Q^*$, which implicitly estimates the optimal value function for a target reward $r$ through a baseline model $\rho_{\texttt{BL}}$ aligned with a baseline reward $\rho_{\texttt{BL}}$ (which can be different from the target reward $r$). Theoretical analyses of Transfer $Q^*$ provide a rigorous characterization of its optimality, deriving an upper bound on the sub-optimality gap and identifying a hyperparameter to control the deviation from the pre-trained reference $\texttt{SFT}$ model based on user needs. Our approach significantly reduces the sub-optimality gap observed in prior SoTA methods and demonstrates superior empirical performance across key metrics such as coherence, diversity, and quality in extensive tests on several synthetic and real datasets.

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Authors (7)

Souradip Chakraborty (36 papers)
Soumya Suvra Ghosal (13 papers)
Ming Yin (70 papers)
Dinesh Manocha (366 papers)
Mengdi Wang (199 papers)
Amrit Singh Bedi (75 papers)
Furong Huang (150 papers)

Citations (7)

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Summary

Transfer Q*: Principled Decoding for LLM Alignment

Introduction

The paper, titled "Transfer Q*: Principled Decoding for LLM Alignment," addresses the crucial challenge of aligning LLMs for safe deployment. Traditional methods of fine-tuning LLMs for alignment, such as reinforcement learning from human feedback (RLHF), involve computing-intensive updates to billions of model parameters. The paper proposes an innovative approach, Transfer Q*, which facilitates alignment directly through decoding instead of model parameter updates.

Problem Scope

Aligning LLMs is a critical step for their deployment in real-world applications where they must adhere to human preferences and ethical standards. Fine-tuning models, while effective, demands substantial computational resources, restricting their scalability and accessibility. Additionally, many state-of-the-art (SoTA) models are not fully open-sourced, further limiting access and practical utility.

Decoding for Alignment

Decoding for alignment emerges as a promising lightweight alternative. Instead of modifying the model parameters, it adjusts the response distribution during the token generation process to align with a target reward function. This method presents several advantages, including reduced computational overhead and adaptability. However, a significant challenge persists: traditional principled decoding methods necessitate access to an oracle for the optimal Q-function ( $Q^*$ ), which is generally infeasible.

Contributions and Innovations

The paper introduces a novel approach called Transfer Q (TQ) to address this challenge. Transfer Q* operates by implicitly estimating the optimal value function for a target reward from a baseline model, $\rho_{BL}$ , which is in alignment with a different, possibly non-overlapping, reward function. This estimation leverages available aligned models, bypassing the need for direct access to $Q^*$ .

Key Contributions

Transfer Decoding Concept: The paper introduces the concept of transfer decoding. By leveraging baseline models aligned with either the target reward (direct transfer) or a different baseline reward (indirect transfer), Transfer Q* efficiently reduces the suboptimality gap observed in prior decoding methods.
Theoretical Characterization: The paper provides a rigorous theoretical evaluation of Transfer Q*. It derives an upper bound on the suboptimality gap in terms of the token-level value function and identifies hyperparameters that control deviation from a pre-trained reference model, allowing for balance based on user needs.
Empirical Evaluation: Extensive empirical tests demonstrate Transfer Q*’s superior performance across metrics like coherence, diversity, and quality. For example, Transfer Q* achieves up to a 1.45x improvement in average reward and a 67.34% increase in the GPT-4-based win-tie rate over existing SoTA methods.

Detailed Methodology

Direct Transfer Decoding

Transfer Q* utilizes aligned models trained via DPO-based methods that align with the target reward. It estimates $Q^{*}$ by leveraging the trajectories generated by these aligned models, eventually deriving the optimal token-level LLM for decoding.

Indirect Transfer Decoding

When baseline models are aligned with a different reward ( $r_{BL}$ ) rather than the target reward ( $r$ ), Transfer Q* employs importance sampling to account for the reward discrepancy. It transforms the baseline-aligned trajectories to estimate the optimal token-level value function for the target reward, ensuring effective and accurate decoding.

Results and Implications

Transfer Q* was evaluated on several datasets and tasks, consistently outperforming existing approaches:

Practical Efficacy: Transfer Q*'s ability to utilize baseline models makes it highly effective and practical for on-the-fly alignment, minimizing computational costs and maximizing efficiency.
Improved Performance: Substantial improvements in achieving target rewards, coherence, and diversity of responses establish Transfer Q* as a superior strategy for implementing principled decoding.
Future Directions: The theoretical and empirical advancements presented in Transfer Q* open avenues for further research, particularly in refining approximation strategies for $Q^{*}$ and exploring more complex reward transformations.

Conclusion

Transfer Q* presents a principled, computationally efficient approach to aligning LLMs via decoding. By leveraging existing aligned models, it circumvents the need for direct updates to LLM parameters, addressing limitations in computational expense and accessibility. The theoretical insights and empirical evidence position Transfer Q* as a foundational method for achieving alignment in LLMs, with significant implications for future developments in AI alignment methodologies.