The Missing Reward: Active Inference in the Era of Experience

TL;DR

This paper advocates for Active Inference as a foundational framework enabling autonomous AI agents to learn from experience without extensive reward engineering, addressing scalability and adaptability challenges in current AI paradigms.

Contribution

It introduces the integration of Active Inference with Large Language Models to create autonomous, adaptable agents that learn from self-generated data, bridging the grounded-agency gap.

Findings

Active Inference can replace external rewards with intrinsic free energy minimization.

Integrating LLMs with AIF enables efficient learning from experience.

Proposes a unified Bayesian framework for autonomous decision-making.

Abstract

This paper argues that Active Inference (AIF) provides a crucial foundation for developing autonomous AI agents capable of learning from experience without continuous human reward engineering. As AI systems begin to exhaust high-quality training data and rely on increasingly large human workforces for reward design, the current paradigm faces significant scalability challenges that could impede progress toward genuinely autonomous intelligence. The proposal for an ``Era of Experience,'' where agents learn from self-generated data, is a promising step forward. However, this vision still depends on extensive human engineering of reward functions, effectively shifting the bottleneck from data curation to reward curation. This highlights what we identify as the \textbf{grounded-agency gap}: the inability of contemporary AI systems to autonomously formulate, adapt, and pursue objectives in response to changing circumstances. We propose that AIF can bridge this gap by replacing external reward signals with an intrinsic drive to minimize free energy, allowing agents to naturally balance exploration and exploitation through a unified Bayesian objective. By integrating Large Language Models as generative world models with AIF's principled decision-making framework, we can create agents that learn efficiently from experience while remaining aligned with human values. This synthesis offers a compelling path toward AI systems that can develop autonomously while adhering to both computational and physical constraints.