A Minimaximalist Approach to Reinforcement Learning from Human Feedback (AI summary)

Key Points

1. The presented algorithm, Self-Play Preference Optimization (SPO), offers a minimalist approach to reinforcement learning from human feedback, as it does not require reward modeling or adversarial training.

2. SPO provably handles non-Markovian, intransitive, and stochastic preferences effectively, making it robust to compounding errors that are common in offline approaches to sequential prediction.

3. The algorithm is built upon the concept of a Minimax Winner (MW) from social choice theory, which frames learning from preferences as a two-player zero-sum game, and achieves strong convergence guarantees.

4. SPO optimizes directly based on preference feedback, without the need for unstable adversarial training, by sampling multiple trajectories from the agent and using the win rate as the reward for a particular trajectory.

5. The SPO algorithm outperforms reward-model based approaches in terms of efficiency, maintaining robustness to intransitive, non-Markovian, and noisy preferences frequently encountered in practice when aggregating human judgments.

6. The algorithm demonstrates effectiveness in fine-tuning large language models and can be applied across various fields such as robotics, recommendation systems, retrieval, and more.

7. The algorithm eliminates the need for training a reward model and is designed to handle noisy and complex preference structures, making it suitable for practical settings.

8. SPO provides a unified approach to optimize a variety of preference structures, including stochastic preferences, non-Markovian preferences, and intransitive preferences.

9. The algorithm computes Minimax Winners efficiently and demonstrates strong performance in challenging setups, illustrating its robustness and practical applicability.

Summary

The research paper introduces a new algorithm called Self-Play Preference Optimization (SPO) for reinforcement learning from human feedback (RLHF). SPO is proposed as a reward-model-free approach that aims to address the limitations of traditional RLHF approaches and provide robustness to various types of preferences. The paper discusses the concept of a Minimax Winner from social choice theory, framing RLHF as a two-player zero-sum game, and leveraging the symmetry of this game to train a single agent in a self-play fashion.

The key contributions of the paper include the derivation and analysis of SPO and its demonstration of performance on continuous control tasks with various types of preference functions. The paper claims that SPO outperforms reward-model based approaches in terms of sample efficiency and robustness to intransitive, non-Markovian, and noisy preferences, as evidenced by empirical results. The research demonstrates the effectiveness of SPO in handling intransitive preferences, matching or exceeding the sample efficiency of reward-model approaches in optimal policy scenarios, robustness to stochastic preferences, and the ability to handle non-Markovian preferences.

Overall, the research presents SPO as a minimalist yet robust approach for reinforcement learning from human feedback, offering significant advantages over traditional reward-model based methods.

Reference: https://arxiv.org/abs/2401.04056