Incoherence in Goal-Conditioned Autoregressive Models
Summary: arXiv:2510.06545v2 Announce Type: replace-cross
Abstract: We investigate mathematically the notion of incoherence: a structural issue with reinforcement learning policies derived by naive goal-conditioning of autoregressive models. We focus on the process of re-training models on their own actions, that is, fine-tuning offline-learned policies with online RL. We prove that it decreases incoherence and leads to an improvement in return, and we aim to characterize the resulting trajectory of policies. By re-framing standard notions of control-as-inference and soft Q learning, we establish a three-way correspondence with two other ways of understanding the iterative re-training process: as folding the posterior into the reward and, in the deterministic case, as decreasing the temperature parameter; the correspondence has computational content via the training-inference trade-off. Through soft-conditioning generative models, we discuss the link between incoherence and the effective horizon.
Introduction
In the rapidly evolving field of artificial intelligence, understanding policy frameworks is crucial for enhancing the performance of algorithms. This article delves into a significant issue termed ‘incoherence’ that arises in the context of goal-conditioned autoregressive models, particularly in reinforcement learning (RL).
Understanding Incoherence
Incoherence refers to structural inconsistencies in the reinforcement learning policies, particularly those that stem from a naive approach to goal-conditioning. This results in policies that may not effectively align with the intended outcomes, leading to suboptimal performance.
Re-training and Policy Improvement
Our investigation highlights the process of re-training models on their own actions. This involves fine-tuning policies that have been developed through offline learning using online reinforcement learning techniques. Key findings include:
- Re-training effectively reduces incoherence.
- This process leads to measurable improvements in return, enhancing overall model performance.
- Characterizing the trajectory of policies post-re-training is essential for understanding the dynamics of these improvements.
Re-framing Control and Learning
By re-framing established concepts such as control-as-inference and soft Q learning, we identify a three-way correspondence with alternative interpretations of the iterative re-training process. These interpretations include:
- Folding the posterior into the reward framework.
- In deterministic scenarios, decreasing the temperature parameter.
- Understanding the computational implications through the training-inference trade-off.
Soft-conditioning Generative Models
The discussion extends to soft-conditioning generative models, revealing a crucial link between incoherence and the concept of effective horizon. This connection emphasizes how incoherence may affect the predictive capabilities and overall efficiency of generative models in various applications.
Conclusion
Addressing incoherence in goal-conditioned autoregressive models is vital for advancing reinforcement learning methodologies. Our findings underscore the importance of iterative re-training and provide a framework for understanding the underlying dynamics that govern policy improvement. By refining our approaches to goal-conditioning, we can enhance the performance and reliability of AI systems across diverse domains.