Improving Reinforcement Learning from Human Feedback Using Contrastive Rewards

แนวคิดหลัก

Contrastive rewards enhance RLHF by improving reward model robustness and performance.

บทคัดย่อ

The content discusses the challenges of using reinforcement learning from human feedback (RLHF) in aligning large language models (LLMs) due to noisy reward models. The proposed method introduces contrastive rewards to improve the effectiveness of RLHF by penalizing reward uncertainty, enhancing robustness, and reducing variance in Proximal Policy Optimization (PPO). Extensive experiments show substantial improvements over strong baselines in both GPT and human evaluations.

สถิติ

Our approach involves two steps: an offline sampling step to obtain responses to prompts and a contrastive reward calculated using baseline responses.
Empirically, contrastive rewards improve RLHF substantially, evaluated by both GPTs and humans.
The proposed method consistently outperforms strong baselines across various tasks evaluated by human annotators.
Mistral-7B-CR model surpasses other models on MT-Bench benchmark with a significant margin.
Mistral-7B-CR demonstrates the lowest Attack Success Rate (ASR) across all red-teaming prompt templates on RED-EVAL benchmark.

คำพูด

"Contrastive rewards enable the LLM to penalize reward uncertainty, improve robustness, encourage improvement over baselines, calibrate according to task difficulty, and reduce variance in PPO."
"Our method consistently outperforms strong baselines across various tasks evaluated by human annotators."
"The recent work on direct preference optimization is one of such efforts among others."

ข้อมูลเชิงลึกที่สำคัญจาก

Improving Reinforcement Learning from Human Feedback Using Contrastive Rewards

by Wei Shen,Xia... ที่ arxiv.org 03-13-2024

https://arxiv.org/pdf/2403.07708.pdf

สอบถามเพิ่มเติม

How can contrastive rewards be adapted for iterative settings in RLHF?

In RLHF, contrastive rewards can be adapted for iterative settings by utilizing the policy obtained from the initial round of policy optimization as the base model for generating contrastive rewards in subsequent rounds. This iterative process involves using the policy learned in each round to refine and improve the reward modeling and policy optimization stages further. By incorporating contrastive rewards iteratively, RL agents can continuously assess their performance based on observed differences between actual rewards and baseline responses, leading to autonomous improvements over multiple iterations.

What are the potential implications of leveraging contrastive rewards for alignment beyond LLMs?

Leveraging contrastive rewards for alignment beyond Large Language Models (LLMs) has several potential implications. Firstly, it can enhance the robustness and effectiveness of reinforcement learning from human feedback (RLHF) across various domains and applications beyond language models. By calibrating reward models through contrastive penalties, this approach can address imperfections in reward modeling systems caused by noisy or ambiguous human preferences.
Furthermore, applying contrastive rewards to other AI systems could lead to more stable training processes with reduced sensitivity to noise or inaccuracies in preference data. This method may also improve generalization capabilities and mitigate issues such as overfitting or reward hacking commonly encountered in reinforcement learning tasks involving human feedback.
Overall, leveraging contrastive rewards for alignment beyond LLMs holds promise for enhancing the reliability, efficiency, and adaptability of AI systems trained through RLHF methodologies across diverse use cases.

How can insights from the noisy label literature be further integrated into improving RLHF methods?

Insights from the noisy label literature offer valuable strategies that can be integrated into improving RLHF methods:

Importance Reweighting: Techniques like importance reweighting from classification with noisy labels help assign appropriate weights to samples based on their reliability. In an RLHF context, this approach could prioritize high-quality human feedback while downweighting uncertain or inconsistent preferences.

Statistical Rejection Sampling: Methods like statistical rejection sampling aim at optimizing preference optimization under uncertainty by filtering out unreliable data points during training iterations.

Learning with Noisy Labels: Leveraging algorithms designed specifically for learning with noisy labels allows RLHF models to better handle imperfect supervision signals inherent in human feedback datasets.

By incorporating these principles into reward modeling processes within RLHF frameworks, practitioners can enhance model robustness against noise-induced biases while improving overall performance accuracy when aligning AI systems with human preferences.