REBEL: Reinforcement Learning via Regressing Relative Rewards (2404.16767v4)

Abstract: While originally developed for continuous control problems, Proximal Policy Optimization (PPO) has emerged as the work-horse of a variety of reinforcement learning (RL) applications, including the fine-tuning of generative models. Unfortunately, PPO requires multiple heuristics to enable stable convergence (e.g. value networks, clipping), and is notorious for its sensitivity to the precise implementation of these components. In response, we take a step back and ask what a minimalist RL algorithm for the era of generative models would look like. We propose REBEL, an algorithm that cleanly reduces the problem of policy optimization to regressing the relative reward between two completions to a prompt in terms of the policy, enabling strikingly lightweight implementation. In theory, we prove that fundamental RL algorithms like Natural Policy Gradient can be seen as variants of REBEL, which allows us to match the strongest known theoretical guarantees in terms of convergence and sample complexity in the RL literature. REBEL can also cleanly incorporate offline data and be extended to handle the intransitive preferences we frequently see in practice. Empirically, we find that REBEL provides a unified approach to LLMing and image generation with stronger or similar performance as PPO and DPO, all while being simpler to implement and more computationally efficient than PPO. When fine-tuning Llama-3-8B-Instruct, REBEL achieves strong performance in AlpacaEval 2.0, MT-Bench, and Open LLM Leaderboard.

• The paper introduces the REBEL algorithm which simplifies RL by regressing relative rewards, eliminating the need for value networks and clipping.
• It proves theoretical equivalence to Natural Policy Gradient, offering robust convergence guarantees and lower computational complexity.
• Empirical evaluations show REBEL’s strong performance in language modeling and image generation, matching or surpassing methods like PPO.

REBEL: A Unified Approach to Language and Image Generation via Relative Reward Regression

Overview of REBEL Algorithm

Reinforcement Learning (RL) techniques have been integral in advancing both natural language and image generation tasks, yet they often require complex elements such as value functions and heuristic strategies to achieve stable training. The paper introduces REBEL (REgression to RElative REward Based RL), a novel RL algorithm that simplifies the training process by converting policy optimization into consecutive least squares regression problems that directly regress relative rewards. This approach eliminates the need for ancillary components like value networks and clipping, which are common in other methods such as Proximal Policy Optimization (PPO).

Theoretical Contributions and Connections

REBEL is positioned as a generalization of standard policy gradient techniques like Natural Policy Gradient (NPG). The authors prove that solving a sequence of squared loss regression tasks (a core mechanism of REBEL) is theoretically analogous to performing iterations of NPG, albeit without requiring the computationally expensive Fisher information matrix. This connection not only simplifies implementation but also enhances computational efficiency.

Formal Guarantees and Implications

The paper delineates strong theoretical guarantees associated with REBEL. It matches some of the strongest known convergence results and sample complexity in RL literature, emphasizing that as long as the regression tasks are solved sufficiently well, the policies produced can compete with any policy covered by the iteratively collected datasets. This robust theoretical basis suggests REBEL could be a versatile tool in both academic research and practical applications.

Empirical Evaluation

Empirical results underscore REBEL’s efficacy in LLMing and image generation tasks. It outperforms or matches leading methods like PPO and Direct Policy Optimization (DPO) across different metrics, including lower computational complexity and memory requirements. The authors conducted comprehensive tests on tasks such as TL;DR summarization and text-guided image generation, using common benchmarks and large-scale models.

LLMing Performance

In LLMing, REBEL demonstrated superior performance in generating summaries when evaluated against human preferences and automated metrics. It achieved this by effectively optimizing a transformer model, illustrating its scalability and robustness in handling complex language tasks.

Image Generation Capabilities

For image generation, REBEL was tested against a consistency model optimized using an aesthetic score predictor. It showed rapid initial improvements and ultimately matched the top performance levels of PPO. This highlights REBEL's capability in quickly adapting to different modalities and optimizing accordingly.

Future Directions

The introduction of REBEL opens several avenues for future research. Its foundational approach, based on regressing relative rewards, presents a scalable alternative to more resource-intensive methods. Further exploration could investigate its application across more varied RL environments, its potential integration with other machine learning paradigms, and its adaptability to more complex multi-agent scenarios or non-standard reward structures.

Concluding Thoughts

Overall, REBEL is presented as a streamlined, theoretically sound approach to RL, particularly effective in generative modeling tasks. By simplifying the RL process while maintaining strong performance, it holds promise for future explorations and practical implementations within the AI and ML communities. This might contribute significantly to the broader adoption of RL techniques in areas where complexity and computational demands have been prohibitive barriers.