Better Rewards Yield Better Summaries: Evaluating Multi-Turn Text Generation with Reinforcement Learning

In this article, researchers present a new benchmark platform called LMRL Gym for evaluating and improving multi-turn reinforcement learning (RTL) algorithms. RTL is a subfield of artificial intelligence that involves training agents to make decisions in complex environments with the goal of maximizing cumulative rewards over time. The LMRL Gym platform provides a variety of tasks and environments for testing and comparing different RTL algorithms, helping to advance the field and identify new approaches that can handle real-world challenges.

Task Definition

The authors define three main tasks in the LMRL Gym platform:

1. Summarizing books with human feedback: In this task, an RTL agent must generate a summary of a given book based on human feedback. The goal is to learn a compact and informative summary that captures the main ideas of the original text.
2. Mining Reddit to learn automatic summarization: This task involves using an RTL agent to automatically summarize posts from the Reddit platform based on user feedback. The goal is to train an RTL agent that can generate high-quality summaries without requiring manual annotation or editing.
3. Scienceworld: Is your agent smarter than a 5th grader?: In this task, an RTL agent must answer questions from a set of science trivia questions at the level of a 5th grader. The goal is to train an RTL agent that can provide accurate and coherent answers to simple scientific questions.

Environments

The LMRL Gym platform provides several environments for testing RTL agents, including:

1. BookSum: A simulated environment that mimics the process of summarizing a book based on human feedback.
2. RedditSum: A simulated Reddit environment where an RTL agent must automatically summarize posts based on user feedback.
3. ScienceWorld: A simulated environment that tests an RTL agent’s ability to answer science trivia questions at the level of a 5th grader.

Reward Functions

The authors propose several reward functions for each task, including:

1. Summarization reward: This reward function encourages the RTL agent to generate summaries that are both informative and coherent with respect to the original text.
2. Automatic summarization reward: This reward function encourages the RTL agent to generate summaries that are accurate and informative without requiring manual editing or annotation.
3. Science trivia reward: This reward function encourages the RTL agent to provide answers to scientific questions that are correct and coherent with respect to the context of the question.

Conclusion

In conclusion, the LMRL Gym platform provides a valuable resource for evaluating and improving multi-turn reinforcement learning algorithms in various tasks and environments. By providing a diverse set of tasks and environments, the platform enables researchers to compare different RTL approaches and identify new strategies that can handle real-world challenges. The proposed reward functions help to focus the development of RTL agents on the most important aspects of each task, such as summarization quality or scientific accuracy. Overall, the LMRL Gym platform is a significant contribution to the field of artificial intelligence and has the potential to drive advances in reinforcement learning research for years to come.