Multi-turn Reinforcement Learning from Preference Human Feedback
(2405.14655)Abstract
Reinforcement Learning from Human Feedback (RLHF) has become the standard approach for aligning LLMs with human preferences, allowing LLMs to demonstrate remarkable abilities in various tasks. Existing methods work by emulating the preferences at the single decision (turn) level, limiting their capabilities in settings that require planning or multi-turn interactions to achieve a long-term goal. In this paper, we address this issue by developing novel methods for Reinforcement Learning (RL) from preference feedback between two full multi-turn conversations. In the tabular setting, we present a novel mirror-descent-based policy optimization algorithm for the general multi-turn preference-based RL problem, and prove its convergence to Nash equilibrium. To evaluate performance, we create a new environment, Education Dialogue, where a teacher agent guides a student in learning a random topic, and show that a deep RL variant of our algorithm outperforms RLHF baselines. Finally, we show that in an environment with explicit rewards, our algorithm recovers the same performance as a reward-based RL baseline, despite relying solely on a weaker preference signal.
Overview
-
The paper introduces a novel Reinforcement Learning (RL) algorithm based on Mirror-Descent to optimize policies for multi-turn dialogues using human preferences, achieving mathematical guarantees of Nash equilibrium.
-
It presents a new evaluation setting, Education Dialogue, where an AI agent acts as a teacher guiding a student, highlighting the algorithm's ability for long-term planning and adaptability.
-
Empirical results demonstrate that the multi-turn RL algorithm outperforms traditional single-turn RL methods and compares favorably with reward-based RL approaches, suggesting broader applications in complex multi-step decision-making problems.
Reinforcement Learning from Human Feedback for Multi-Turn Dialogues
Introduction to the Problem
Reinforcement Learning from Human Feedback (RLHF) has successfully been used to align LLMs with human preferences. But there's a catch: these methods generally focus on single-turn interactions. This setup only teaches the model to take optimal actions based on immediate feedback, not considering long-term planning that's crucial for tasks that require several steps or interactions, like multi-turn dialogues.
Core Contributions and Techniques
The paper tackles the challenge of extending RLHF to multi-turn settings where the feedback is collected on entire dialogues rather than individual turns. Here’s a quick rundown of the key contributions:
- Novel RL Algorithm: Introducing a Mirror-Descent-based policy optimization algorithm for multi-turn, preference-based RL, which has mathematical guarantees to find balance in a competitive setting (known as Nash equilibrium).
- New Environment for Validation: Developing a new evaluation environment called Education Dialogue, where an AI agent plays the role of a teacher guiding a student through a topic. It’s designed to test the model’s ability for long-term planning and adaptability.
- Performance Comparison: Demonstrating that their multi-turn RL algorithm outperforms traditional single-turn RLHF methods and achieves comparable results to reward-based RL methods, even though it relies on weaker preference signals alone.
Breaking Down the Technical Details
Contextual Markov Decision Process (CMDP)
The interaction between an AI agent and its environment is captured in the contextual RL model:
- Contextual Markov Decision Process (CMDP): Represents the sequence of decisions an agent makes, aiming to optimize long-term outcomes.
- Regularized Preference Model: Instead of mapping preferences to rewards explicitly, a regularized model is used to capture preferences directly, elegantly managing uncertainties and ensuring robust policy updates.
The Multi-Turn Setting
Rather than evaluating actions on a per-turn basis, the paper focuses on preferences between entire dialogues. This is crucial for tasks where the impact of certain actions is only evident after several exchanges.
A practical example provided is:
- Chatbot Negotiation: In a negotiation dialogue, offering a high price for a product might seem immediately unfavorable, but if it's part of a strategy to negotiate down to an acceptable price, it might be beneficial in the end.
Innovative Algorithms
Multi-turn Preference Optimization (MTPO): This core algorithm updates policies based on the mirror descent method coupled with self-play, ensuring convergence to a Nash equilibrium:
- Preference-Based Q-Function: Extends value functions (used in traditional RL) to the multi-turn preference setting. This allows capturing the long-term consequences of choices.
- MTPO Variants: Including MTPO-$\tau$ which involves mixture policies for enhanced performance in practice.
Multi-turn RLHF: The framework also adapts to settings where reward functions can be learned from preferences, providing a more traditional RLHF approach for comparison.
Experiments and Results
Education Dialogue Environment
Introduced a novel environment where a teacher (the AI agent) interacts with a student to help them learn a topic. Here:
- Preference feedback: Dialogues are judged based on overall educational effectiveness rather than immediate responses.
- Outstanding results: MTPO demonstrated a significant improvement over single-turn RLHF methods in this setting.
Car Dealer Environment
Used a pre-existing LMRL-Gym environment where the agent (car dealer) negotiates with a customer:
- Performance Comparison: Even in reward-rich environments, preference-based learning showed robust performance comparable to reward-based RL methods.
Practical and Theoretical Implications
Practical Aspects
- Enhanced Conversational AI: Enhancing multi-turn capabilities can lead to more sophisticated and adaptive conversational agents, improving interactions in customer service, tutoring systems, and negotiation bots.
- Broader Applications: Beyond dialogues, these methods can be applied to any multi-step decision-making problem, such as robotic control and complex game strategies.
Theoretical Insights
- Extends the Scope of RL: Proving convergence to Nash equilibrium in multi-turn settings underlines the robustness and reliability of the proposed methods, pushing the boundaries of current RL theories.
- Adaptive Learning: Emphasizing direct preference optimization opens pathways to explore and exploit new forms of feedback less reliant on explicit reward functions.
Future Directions
Future research could delve into:
- Combining Turn-Level and Token-Level Optimizations: Investigating ways to integrate fine-grained (token-level) preferences with broader (turn-level) ones.
- Real-World Feedback Collection: Further validation in real-world settings where human feedback can be noisy, diverse, and context-dependent.
In conclusion, this work bridges a crucial gap in reinforcement learning by extending preference-based RL to multi-turn interactions, backed with strong theoretical guarantees and promising empirical results. The practical implications for conversational AI and beyond are vast, making it an exciting advancement in the field.
Create an account to read this summary for free: