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EVOLvE: Evaluating and Optimizing LLMs For In-Context Exploration
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Despite their success in many domains, large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. This is crucial as many real-world applications, ranging from personalized recommendations to healthcare interventions, demand that LLMs not only predict but also actively learn to make optimal decisions through exploration. In this work, we measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications. We develop a comprehensive suite of environments, including both context-free and contextual bandits with varying task difficulties, to benchmark LLMs' performance. Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs: by providing explicit algorithm-guided support during inference; and through algorithm distillation via in-context demonstrations and fine-tuning, using synthetic data generated from these algorithms. Impressively, these techniques allow us to achieve superior exploration performance with smaller models, surpassing larger models on various tasks. We conducted an extensive ablation study to shed light on various factors, such as task difficulty and data representation, that influence the efficiency of LLM exploration. Additionally, we conduct a rigorous analysis of the LLM's exploration efficiency using the concept of regret, linking its ability to explore to the model size and underlying algorithm.
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Cited by 7 Pith papers
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Formalizing Learning from Language Feedback with Provable Guarantees
Introduces a formal framework for learning from language feedback, a transfer eluder dimension complexity measure, and HELiX, a no-regret algorithm whose regret scales with this dimension.
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ABBEL trains LLM agents to act from a compact natural-language belief state; belief-quality and brevity rewards close most of the gap with full-context agents.
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Behavioral Exploration: Learning to Explore via In-Context Adaptation
A coverage-conditioned behavioral cloning policy adapts in-context to its own history, making robots explore new expert-like behaviors online without online reinforcement learning.
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e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs
e3 couples asymmetric skill chaining, negative-gradient RL, and a difficulty/budget curriculum so a 1.7B model extrapolates test-time compute to 2x its training budget and sets reported <2B state-of-the-art on AIME/HMMT 2025.
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LLM-First Search: Self-Guided Exploration of the Solution Space
LLM-First Search, where the model itself decides whether to continue or backtrack during reasoning, outperforms MCTS, BestFS, and ToT-BFS on harder Countdown and Sudoku tasks while using fewer tokens.
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Large Language Models Think Too Fast To Explore Effectively
In Little Alchemy 2, most LLMs discover fewer elements than humans and rely on uncertainty rather than empowerment; reasoning models o1 and DeepSeek-R1 explore more effectively.
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Disentangling Exploration of Large Language Models by Optimal Exploitation
Exploration by LLM agents can be measured separately from exploitation using an optimal exploitation oracle, and most models explore poorly, with exploration performance correlated to reasoning ability.
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