REBEL: Reinforcement Learning via Regressing Relative Rewards
(2404.16767)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 rewards via a direct policy parameterization between two completions to a prompt, 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 handle the intransitive preferences we frequently see in practice. Empirically, we find that REBEL provides a unified approach to language modeling and image generation with stronger or similar performance as PPO and DPO, all while being simpler to implement and more computationally tractable than PPO.
Overview
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The REBEL algorithm introduces a streamlined approach to reinforcement learning by regressing relative rewards, removing the need for extra components like value networks.
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REBEL provides a strong theoretical foundation, showcasing equivalent capabilities to established policy gradient techniques but with enhanced efficiency and simplified computations.
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Empirical evaluations in language and image generation tasks show that REBEL matches or exceeds other leading methods with less computational load, highlighting its practical effectiveness and potential for broader applications.
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 language modeling 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.
Language Modeling Performance
In language modeling, 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.
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