Objective Mismatch in Reinforcement Learning from Human Feedback: Conclusion

Table of Links

Abstract & Introduction

Related Work

Background

Understanding Objective Mismatch

Discussions

Conclusion

Acknowledgments, and References

6 Conclusion

This paper presents the multiple ways by which objective mismatch limits the accessibility and reliability of RLHF methods. This current disconnect between design a reward model, optimizing it, and the downstream model goals creates a method that is challenging to implement and improve on. Future work mitigating mismatch and the proxy objectives present in RLHF, LLMs and other popular machine learning methods will becomes easier to align with human values and goals, solving many common challenges users encounter with state-of-the-art LLMs.