Imitating Language via Scalable Inverse Reinforcement Learning (2409.01369v2)

Abstract: The majority of LLM training builds on imitation learning. It covers pretraining, supervised fine-tuning, and affects the starting conditions for reinforcement learning from human feedback (RLHF). The simplicity and scalability of maximum likelihood estimation (MLE) for next token prediction led to its role as predominant paradigm. However, the broader field of imitation learning can more effectively utilize the sequential structure underlying autoregressive generation. We focus on investigating the inverse reinforcement learning (IRL) perspective to imitation, extracting rewards and directly optimizing sequences instead of individual token likelihoods and evaluate its benefits for fine-tuning LLMs. We provide a new angle, reformulating inverse soft-Q-learning as a temporal difference regularized extension of MLE. This creates a principled connection between MLE and IRL and allows trading off added complexity with increased performance and diversity of generations in the supervised fine-tuning (SFT) setting. We find clear advantages for IRL-based imitation, in particular for retaining diversity while maximizing task performance, rendering IRL a strong alternative on fixed SFT datasets even without online data generation. Our analysis of IRL-extracted reward functions further indicates benefits for more robust reward functions via tighter integration of supervised and preference-based LLM post-training.

• The paper reformulates inverse soft Q-learning as a TD-regularized extension of MLE to mitigate compounding errors in sequential language generation.
• Experiments on T5 and PaLM2 architectures show that IRL boosts task performance and increases output diversity compared to standard methods.
• IRL delivers robust reward extraction and improved long-term dependency handling, paving the way for more adaptive, human-like text generation.

Imitating Language via Scalable Inverse Reinforcement Learning

The paper under discussion, titled "Imitating Language via Scalable Inverse Reinforcement Learning," proposes an innovative approach to fine-tune LLMs through the lens of inverse reinforcement learning (IRL). This work, authored by researchers at Google DeepMind, critiques the predominant paradigm of Maximum Likelihood Estimation (MLE) used in next-token prediction and explores IRL as a potential alternative to enhance model performance and diversity.

Overview

The central thesis of the paper is that imitation learning's current dependence on MLE, although simple and scalable, does not harness the full potential of sequential structures in autoregressive models. MLE can incur issues such as compounding errors and exposure bias, especially with longer token sequences. The authors argue that by considering the impact of future actions via dynamics-aware optimization, IRL can address these issues more effectively.

Key Contributions

1. Reformulation of Inverse Soft Q-Learning: The authors reformulate inverse soft-Q-learning as a temporal difference (TD) regularized extension of MLE. This creates a principled connection between MLE and IRL, facilitating a balance between added computational complexity and enhanced performance.
2. Evaluation Across Model Sizes: They conduct extensive experiments on models ranging from 250 million to 3 billion parameters, specifically using the T5 and PaLM2 architectures. These experiments assess both task performance and the diversity of model generations.
3. Comparison with MLE and Other IRL Methods: The paper compares standard MLE-based fine-tuning with IRL methods, including both adversarial and non-adversarial approaches, to underline when and how IRL can act as an effective alternative.
4. Insights on Robustness and Data Efficiency: Their analysis indicates that IRL not only improves performance metrics but also retains the diversity of generated sequences better than MLE. This robustness is crucial for aligning LLMs with human preferences.

Numerical Results and Claims

The paper provides compelling evidence that IRL-based methods outperform MLE in several key areas:

• Task Performance: On benchmarks like GSM8k and XSUM, IRL methods exhibit clear advantages in terms of task accuracy and other performance metrics.
• Diversity of Generations: IRL methods, particularly IQLearn, demonstrate increased diversity in model outputs, as measured by lower self-similarity scores (Self-BLEU).

The paper also highlights that even under offline settings—where data generation is static and does not adapt to agent interactions—IRL methods still offer significant improvements over MLE.

Practical and Theoretical Implications

Practically, the shift towards IRL can enhance LLMs in several ways:

• Improved Generalization: By optimizing sequences rather than individual tokens, IRL can better handle the long-term dependencies inherent in natural language.
• Enhanced Diversity: This contributes to generating more varied and human-like text, which can be advantageous for applications requiring creativity and adaptability.
• Robust Reward Functions: Better reward signal extraction could also facilitate more effective subsequent stages of model training, such as reinforcement learning from human feedback (RLHF).

Theoretically, this research bridges a critical gap between supervised learning and reinforcement learning, proposing a new paradigm that incorporates the benefits of both. By framing language generation as a sequential decision-making problem, it opens up avenues for further research in IRL and its applications in other areas of AI.

Future Developments

The implications of this work suggest several promising directions for future research:

• Enhanced Model Training Pipelines: Integrating IRL into standard LLM training pipelines, potentially combined with RLHF stages, could lead to more robust and adaptable models.
• Exploration of Other IRL Algorithms: Expanding the repertoire of IRL methods beyond IQLearn and GAIL could provide deeper insights and further performance gains.
• Scalability and Efficiency: Addressing the computational overheads associated with online data generation in IRL could make these methods more accessible for large-scale applications.

Conclusion

This paper puts forth a compelling case for rethinking current LLM fine-tuning strategies by leveraging inverse reinforcement learning. By systematically comparing IRL to traditional MLE-based methods and demonstrating clear advantages in both performance and diversity, it sets the stage for broader adoption and further innovation in the field of LLMs. The integration of IRL into the LLM training pipeline represents a significant step towards more generalized, robust, and adaptable AI systems.