Imitating Language via Scalable Inverse Reinforcement Learning

🌈 Abstract

The article investigates the use of inverse reinforcement learning (IRL) methods as an alternative to maximum likelihood estimation (MLE) for fine-tuning large language models (LLMs). The key points are:

• Language generation can be modeled as a sequential decision-making problem, and IRL methods can be used to extract rewards and directly optimize sequences instead of individual token likelihoods.
• The authors reformulate inverse soft-Q-learning as a temporal difference regularized extension of MLE, creating a principled connection between MLE and IRL.
• Experiments show that IRL-based imitation can provide clear advantages, particularly in retaining diversity while maximizing task performance, compared to standard MLE fine-tuning.
• The analysis of IRL-extracted reward functions indicates potential benefits for more robust reward functions via tighter integration of supervised and preference-based LLM post-training.

🙋 Q&A

[01] Introduction

1. What are the key mechanisms underlying increasingly capable and general artificial intelligence systems? The key mechanisms are pretraining and supervised fine-tuning of large language models (LLMs), which predominantly rely on imitation learning, in particular next token prediction via maximum likelihood estimation (MLE).

2. How has the perspective on language modeling shifted recently? The perspective has shifted towards explicitly treating language modeling as a sequential decision making problem, particularly for later stages of model adaptation via reinforcement learning from human feedback (RLHF).

3. What are the potential benefits of the RL perspective to imitation for language modeling? The RL perspective opens up new opportunities for the effective use of different data sources, obtaining aligned models that better represent human intent, and dynamics-aware optimization of each action based on its future impact.

[02] Methods

1. How does the distribution matching perspective of inverse reinforcement learning (IRL) differ from maximum likelihood estimation (MLE) for language generation? IRL algorithms seek to minimize the divergence between the discounted state-action distribution of the policy and the discounted state-action distribution of the expert policy, in contrast to MLE which maximizes the likelihood of the training sequences.

2. How does the reformulation of inverse soft-Q-learning establish a connection between MLE and IRL? The reformulation shows that IRL can be seen as performing maximum likelihood with a dynamics-dependent temporal difference regularization term, explicitly bridging between MLE and algorithms exploiting the sequential nature of language generation.

3. What are the key advantages of the reformulated IQLearn objective compared to adversarial IRL methods like GAIL? The reformulated IQLearn objective does not require samples from the current policy but uses expert samples only, and allows annealing of the regularization term to flexibly trade off between standard MLE and IRL.

[03] Experiments

1. What are the key findings from the comparison of MLE and IRL methods for fine-tuning LLMs? The experiments demonstrate that IRL-based methods can achieve better or on par task performance compared to MLE, while significantly increasing the diversity of model generations. Offline IRL methods in particular show strong performance gains over MLE.

2. How do the online and offline versions of IQLearn compare in terms of performance and computational requirements? The offline version of IQLearn is computationally cheaper as it does not require online sampling, while the online version can provide slightly better performance by incorporating additional non-expert samples. The choice depends on the practitioner's trade-off between computational cost and potential performance gains.

3. What insights does the analysis of IRL-extracted reward functions provide? The analysis indicates that the rewards extracted via IRL methods contain useful information about task performance, suggesting their potential value for aiding later stages of RLHF or RLAIF training.