TMLR Journal 2025 Journal Article
Exploring exploration with foundation agents in interactive environments
- Daniel P. Sawyer
- Nan Rosemary Ke
- Hubert Soyer
- Martin Engelcke
- John Reid
- David P Reichert
- Drew A. Hudson
- Alexander Lerchner
Foundation models excel at single-turn reasoning, but many real-world challenges, from scientific research to technology development, require multi-turn exploration in dynamic interactive environments. Crucial components of learning from experience in these settings, such as efficiently gathering information to test hypotheses, meta-learning a model of the world's dynamics, and adapting to unexpected changes, remain largely unexplored for these models. We first evaluate foundation models in Feature World, a setting that primarily tests information gathering about a static hidden reward function. In this initial setting, we show that state-of-the-art foundation models come close to optimal efficiency in selecting maximally informative actions in tasks with simple reward functions. As a proof of concept, we also show a model can gather information efficiently in a 3D embodied version of this task, though errors in vision limit some aspects of performance. In order to test exploration across multiple dependent turns and trials, we implement a custom, text-based version of the Alchemy environment, a benchmark designed for meta-learning. Here, agents must deduce a latent causal structure by integrating information across multiple state-dependent trials. In this more complex setting, we find that recent foundation models struggle to meta-learn strategies that enable improved performance over time. However, prompting the models to summarize their observations at regular intervals enables an emergent meta-learning process, allowing them to improve across trials. Notably, in some models, summarization also enabled adaptive re-learning of this information when the environment's rules change unexpectedly. While most models performed reasonably well on simple Feature World tasks, evaluations in Alchemy reveal stark differences in robustness among the models, with Gemini 2.5 performing best, followed by Claude 3.7, and ChatGPT-4o and o4-mini struggling the most. These results underscore Alchemy's value as a benchmark for meta-learning and strategy adaptation in foundation models. By moving beyond simple discovery to complex, stateful environments, we demonstrate that the most significant challenge for foundation agents is not selecting informative actions in the moment, but rather seeking and integrating knowledge through adaptive strategies over time. Intriguingly, we find there is likely no intrinsic barrier to future generations of foundation agents more fully mastering these abilities.