Research map / Multi-agent systems and games / LLM agents
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems
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Provides a theoretical framework (Planner-Actor-Reporter) showing LLM planners perform Bayesian aggregated imitation learning in context, that naive subgoal following incurs linear regret, and that an epsilon-greedy exploration yields sublinear regret with pretraining-error terms.
Problem
Understanding why LLM-empowered agents can solve decision-making problems in the physical world, specifically the regret and exploration-exploitation behavior of an LLM Planner interacting with a low-level Actor and a Reporter.
Approach
Models the LLM agent as a hierarchical RL system (high-level POMDP for the LLM Planner, low-level MDP for the Actor), proves that LLM prompting implements Bayesian aggregated imitation learning, and adds an epsilon-greedy exploration over LLM subgoals to obtain sublinear regret under pretraining error.
Key results
- Naively executing LLM-returned subgoals incurs linear regret (Proposition on hard-to-distinguish example: Reg >= 0.5T(1-1/|Z|)^T).
- Epsilon-greedy LLM planning achieves Reg = O~(H^{3/2} sqrt(T/eta log(c_Z|Z|sqrt T))) in the perfect setting.
- Under practical setting the regret adds an H^2 T times cumulative pretraining error term, which vanishes with more pretraining data.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add an $\epsilon$-greedy exploration policy to LLM-recommended BAIL subgoals | LLM-driven Planner in an HMDP · HMDP, perfect setting | regret: ${\rm Reg}_z(T)\geq 0.5T\cdot(1-1/|\mathcal{Z}|)^T$ -> ${\rm Reg}_z(T)\leq\tilde{\mathcal{O}}\left(H^\frac{3}{2}\sqrt{T/ | Helped |
| use an $\epsilon$-greedy exploration policy with a pretrained PAR system | LLM-driven Planner in an HMDP · HMDP, practical setting | regret: ${\rm Reg}_z(T)\leq\tilde{\mathcal{O}}\Big(H^\frac{3}{2}\sqrt{T/\eta\cdot\log(c_\mathcal{Z}|\mathcal{Z}|\sqrt{T})}+H^2T\ | Mixed |
| use LLM as a world model to predict observations instead of directly generating subgoals | LLM-driven Planner · HMDP with LLM as world model | regret: ${\rm Reg}_z(T)\leq\tilde{\mathcal{O}}(H\sqrt{T/\eta})+H^2T\cdot\Delta_{\rm p,wm}$ | Mixed |
| coordinate a collaborative team of $K$ Actors | LLM-driven Planner · Cooperative HMG, perfect setting | regret: ${\rm Reg}_z(T)\leq\tilde{\mathcal{O}}(\sqrt{H^3TK/\eta})$ | Mixed |
| increase the volume of LLM and Reporter pretraining data | Pretrained PAR system · HMDP, practical setting | regret | Helped |
Surprising findings
- Even with expert-guided LLM pretraining, pure LLM-guided planning without exploration can suffer linear regret.
Limitations
- Purely theoretical; no empirical experiments on real LLM agents reported in the text
- Requires strong assumptions such as realizability, bounded boundedness, ambiguity, and coverage
Experimental setup
Metrics: regret
Baselines: Naive LLM-subgoal following (strict LLM policy)
Builds on: Decision-pretrained transformer (DPT) Hierarchical reinforcement learning Bayesian model averaging Noise contrastive estimation
Closest papers by meaning
- Can large language models explore in-context? (NeurIPS 2024 · similarity 0.91)
- $\texttt{Multi}^2$: Hierarchical Multi-Agent Decision-Making with LLM-Based Agents in Interactive Environments (ICML 2026 · similarity 0.91)
- Expanding LLM Agent Boundaries with Strategy-Guided Exploration (NeurIPS 2026 · similarity 0.91)
- Scaling Autonomous Agents via Automatic Reward Modeling And Planning (ICLR 2025 · similarity 0.91)
- EVOLvE: Evaluating and Optimizing LLMs For In-Context Exploration (ICML 2025 · similarity 0.90)
- Reinforcement World Model Learning for LLM-based Agents (NeurIPS 2026 · similarity 0.90)
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