Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization (2210.01241v3)

Abstract: We tackle the problem of aligning pre-trained LLMs (LMs) with human preferences. If we view text generation as a sequential decision-making problem, reinforcement learning (RL) appears to be a natural conceptual framework. However, using RL for LM-based generation faces empirical challenges, including training instability due to the combinatorial action space, as well as a lack of open-source libraries and benchmarks customized for LM alignment. Thus, a question rises in the research community: is RL a practical paradigm for NLP? To help answer this, we first introduce an open-source modular library, RL4LMs (Reinforcement Learning for LLMs), for optimizing language generators with RL. The library consists of on-policy RL algorithms that can be used to train any encoder or encoder-decoder LM in the HuggingFace library (Wolf et al. 2020) with an arbitrary reward function. Next, we present the GRUE (General Reinforced-language Understanding Evaluation) benchmark, a set of 6 language generation tasks which are supervised not by target strings, but by reward functions which capture automated measures of human preference. GRUE is the first leaderboard-style evaluation of RL algorithms for NLP tasks. Finally, we introduce an easy-to-use, performant RL algorithm, NLPO (Natural Language Policy Optimization) that learns to effectively reduce the combinatorial action space in language generation. We show 1) that RL techniques are generally better than supervised methods at aligning LMs to human preferences; and 2) that NLPO exhibits greater stability and performance than previous policy gradient methods (e.g., PPO (Schulman et al. 2017)), based on both automatic and human evaluations.

An Overview of Reinforcement Learning for LLM Alignment

The research paper titled "Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization" addresses the viability and effectiveness of applying reinforcement learning (RL) techniques to the domain of NLP, specifically for the alignment of LLMs (LMs) with human preferences. This investigation is undertaken in light of the inherent challenges posed by the application of RL in LLM generation, such as training instability and the complexity of the combinatorial action space.

Core Contributions

The authors present several key contributions that aim to evaluate and enhance the applicability of RL in NLP:

1. RL4LMs Library: The paper introduces an open-source, modular library named RL4LMs, which facilitates the training of LLMs using on-policy reinforcement learning algorithms. This library supports any encoder or encoder-decoder LLM from the HuggingFace repository, providing a platform for optimizing language generation with custom reward functions.
2. GRUE Benchmark: The General Reinforced-language Understanding Evaluation (GRUE) benchmark is introduced as the first leaderboard-style evaluation of RL algorithms for language generation tasks. It encompasses six tasks that are overseen by reward functions reflecting automated measures of human preference, as opposed to traditional supervised methods relying on target strings. This benchmark serves to objectively assess the performance of RL approaches in aligning LLMs with human preferences.
3. NLPO Algorithm: The authors propose a new RL algorithm termed Natural Language Policy Optimization (NLPO). This algorithm is designed to efficiently manage the combinatorial action space inherent in language generation tasks. Experimental results indicate that NLPO outperforms traditional policy gradient methods like Proximal Policy Optimization (PPO) in terms of stability and performance.

Empirical Findings

The paper provides evidence that RL techniques can surpass supervised methods in aligning LLMs to human preferences, particularly through the utilization of reward functions that are tailored to capture human-like evaluations. Furthermore, the NLPO algorithm is demonstrated to achieve superior results compared to existing methods, as validated by both automated metrics and human evaluations.

Implications and Future Directions

This paper has significant implications for both the theoretical understanding and practical application of RL in natural language generation. The introduction of the RL4LMs library and GRUE benchmark provides researchers with the necessary tools and standardized metrics to explore and refine RL methodologies for LLM alignment. The success observed with the NLPO algorithm suggests potential for further exploration and enhancement of RL strategies in managing the complexities of natural language tasks.

Looking forward, this research opens several avenues for future exploration, including the development of more sophisticated reward functions that can capture nuanced human preferences, as well as the extension of RL applications to broader NLP domains beyond language generation. Moreover, continued efforts in reducing the computational demands and enhancing the scalability of RL methods would be valuable in promoting more widespread adoption and integration into real-world NLP systems.