Insights from the Inverse: Reconstructing LLM Training Goals Through Inverse RL (2410.12491v1)

Abstract: LLMs trained with Reinforcement Learning from Human Feedback (RLHF) have demonstrated remarkable capabilities, but their underlying reward functions and decision-making processes remain opaque. This paper introduces a novel approach to interpreting LLMs by applying inverse reinforcement learning (IRL) to recover their implicit reward functions. We conduct experiments on toxicity-aligned LLMs of varying sizes, extracting reward models that achieve up to 80.40% accuracy in predicting human preferences. Our analysis reveals key insights into the non-identifiability of reward functions, the relationship between model size and interpretability, and potential pitfalls in the RLHF process. We demonstrate that IRL-derived reward models can be used to fine-tune new LLMs, resulting in comparable or improved performance on toxicity benchmarks. This work provides a new lens for understanding and improving LLM alignment, with implications for the responsible development and deployment of these powerful systems.

Insights from the Inverse: Reconstructing LLM Training Goals Through Inverse RL

The paper "Insights from the Inverse: Reconstructing LLM Training Goals Through Inverse RL" presents an innovative approach to uncovering the implicit reward functions of LLMs trained via Reinforcement Learning from Human Feedback (RLHF). The authors leverage Inverse Reinforcement Learning (IRL) to extract these reward functions, aiming to enhance the interpretability and alignment of LLMs.

Overview

LLMs, particularly those fine-tuned using RLHF, demonstrate notable performance but conceal the reward functions driving their decision-making processes. This opacity poses significant challenges in ensuring alignment and safety, especially in high-stakes applications such as healthcare and criminal justice. The authors propose using IRL to address this issue, reconstructing the reward functions underlying LLMs' behavior.

Methodology

The authors developed a comprehensive methodology involving the use of IRL to recover LLM reward functions. The process focuses on toxicity-aligned models of different scales, employing Maximum Margin IRL to approximate reward functions. These models were trained on a curated version of the Jigsaw toxicity dataset, leveraging a RoBERTa-based reward model for initial RLHF fine-tuning. The authors utilized Pythia models of varying sizes (70M and 410M parameters) to assess the scalability and effectiveness of their approach.

Key Findings

1. Accuracy and Correlation: The extraction process achieved up to 80.40% accuracy in predicting human preferences for the 70M model. However, the authors highlight that traditional correlation metrics may not fully capture model efficacy, suggesting a nuanced approach is necessary.
2. Model Performance: IRL-derived reward models facilitated the fine-tuning of new LLMs with comparable or improved toxicity reduction. Specifically, the IRL-RLHF models achieved lower toxicity outputs than their SFT and original RLHF counterparts.
3. Non-identifiability Challenges: A key difficulty observed was the non-identifiability of reward functions, marked by variability in accuracy across different training runs. This points to fundamental issues in reward learning, where multiple reward models can yield similar behavioral outputs.

Implications

The paper presents noteworthy implications for AI safety and interpretability. By revealing underlying reward models, the approach enhances transparency and accountability in LLM deployment. Furthermore, understanding reward structures aids in assessing potential vulnerabilities and improving model reliability. Addressing non-identifiability challenges remains critical for future research, as it affects the replicability and robustness of extracted reward functions.

Future Directions

The authors advocate exploring more scalable IRL techniques to manage larger model sizes exceeding billions of parameters. Additionally, focusing on complex reward landscapes, such as multi-objective functions, may provide a more holistic understanding of LLM behavior. The implications for adversarial robustness and bias detection represent valuable avenues for future work.

Conclusion

This paper contributes a novel perspective on LLM interpretability through IRL, offering a method to elucidate the often-opaque reward functions encoded in RLHF-trained models. While challenges remain, particularly regarding scalability and non-identifiability, the proposed approach paves the way for improved alignment and safer deployment of LLMs. The findings underscore the need for ongoing research into advanced IRL methodologies to fully realize the potential of these powerful AI systems.