Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Inverse-Q*: Token Level Reinforcement Learning for Aligning Large Language Models Without Preference Data

Han Xia, Songyang Gao, Qiming Ge, Zhiheng Xi, Qi Zhang, Xuanjing Huang

TL;DR

RLHF methods like PPO rely on reward models and complex optimization, which can hinder sample efficiency and stability. Inverse-Q* introduces token-level reinforcement learning without additional reward or value models by estimating a conditionally optimal policy $\pi^*(\cdot|\cdot)$ and performing reward imitation with auto reward assignment, aligning model outputs via token-level credit signals. Empirically, it matches or exceeds PPO in convergence speed and alignment quality across 7B/13B models on Anthropic-RLHF-HH and BeaverTail datasets, while reducing labeling and supervision needs. This approach offers a practical, robust alternative for efficient, low-resource RLHF, with token-level supervision that scales to diverse sampling outcomes and models in large-language-model alignment contexts.

Abstract

Reinforcement Learning from Human Feedback (RLHF) has proven effective in aligning large language models with human intentions, yet it often relies on complex methodologies like Proximal Policy Optimization (PPO) that require extensive hyper-parameter tuning and present challenges in sample efficiency and stability. In this paper, we introduce Inverse-Q*, an innovative framework that transcends traditional RL methods by optimizing token-level reinforcement learning without the need for additional reward or value models. Inverse-Q* leverages direct preference optimization techniques but extends them by estimating the conditionally optimal policy directly from the model's responses, facilitating more granular and flexible policy shaping. Our approach reduces reliance on human annotation and external supervision, making it especially suitable for low-resource settings. We present extensive experimental results demonstrating that Inverse-Q* not only matches but potentially exceeds the effectiveness of PPO in terms of convergence speed and the alignment of model responses with human preferences. Our findings suggest that Inverse-Q* offers a practical and robust alternative to conventional RLHF approaches, paving the way for more efficient and adaptable model training approaches.

Inverse-Q*: Token Level Reinforcement Learning for Aligning Large Language Models Without Preference Data

TL;DR

RLHF methods like PPO rely on reward models and complex optimization, which can hinder sample efficiency and stability. Inverse-Q* introduces token-level reinforcement learning without additional reward or value models by estimating a conditionally optimal policy and performing reward imitation with auto reward assignment, aligning model outputs via token-level credit signals. Empirically, it matches or exceeds PPO in convergence speed and alignment quality across 7B/13B models on Anthropic-RLHF-HH and BeaverTail datasets, while reducing labeling and supervision needs. This approach offers a practical, robust alternative for efficient, low-resource RLHF, with token-level supervision that scales to diverse sampling outcomes and models in large-language-model alignment contexts.

Abstract

Reinforcement Learning from Human Feedback (RLHF) has proven effective in aligning large language models with human intentions, yet it often relies on complex methodologies like Proximal Policy Optimization (PPO) that require extensive hyper-parameter tuning and present challenges in sample efficiency and stability. In this paper, we introduce Inverse-Q*, an innovative framework that transcends traditional RL methods by optimizing token-level reinforcement learning without the need for additional reward or value models. Inverse-Q* leverages direct preference optimization techniques but extends them by estimating the conditionally optimal policy directly from the model's responses, facilitating more granular and flexible policy shaping. Our approach reduces reliance on human annotation and external supervision, making it especially suitable for low-resource settings. We present extensive experimental results demonstrating that Inverse-Q* not only matches but potentially exceeds the effectiveness of PPO in terms of convergence speed and the alignment of model responses with human preferences. Our findings suggest that Inverse-Q* offers a practical and robust alternative to conventional RLHF approaches, paving the way for more efficient and adaptable model training approaches.
Paper Structure (20 sections, 1 theorem, 10 equations, 10 figures, 1 table, 1 algorithm)

This paper contains 20 sections, 1 theorem, 10 equations, 10 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Reinforcement Learning from Human Feedback
  4. Credit Assignment
  5. Self-Improvement
  6. Preliminaries
  7. Methods
  8. Policy Optimization Through Reward Imitation
  9. Reward Imitation Performs Auto Reward Assignment
  10. Experiments
  11. Experimental Settings
  12. Main Results
  13. Analysis Experiments
  14. Ablation Studies
  15. Discussions
  16. ...and 5 more sections

Key Result

Lemma 4.1

Considering two policies $\pi_a$ and $\pi_b$ where $\pi_a$ is superior, meaning $V(\pi_a; \mathbf{y}_s) > V(\pi_b; \mathbf{y}_s)$, for any imitation policy $\pi_\theta = (1-\delta) \cdot \pi_b + \delta \cdot (\pi_b - \pi_a)$, where $\delta$ is any real number in the interval (0, 1), it holds that $V

Figures (10)

  • Figure 1: Existing model alignment approaches require preference data for reward modeling. However, Inverse Q* utilize reward imitation from superior strategies to achieve token-level credit assignment, making model alignment more efficient without preference data.
  • Figure 2: Ours vs. baselines on Zephyr-7B-SFT
  • Figure 3: Ours vs. baselines on Vicuna-7B
  • Figure 4: Ours vs. baselines on Vicuna-13B
  • Figure 6: The win-rate of our method on Zephyr-7B-beta against the original Zephyr-7B-SFT and the one prompted with positive principle.
  • ...and 5 more figures

Theorems & Definitions (2)

  • Lemma 4.1: Reward Imitatioin
  • proof