SGA-MCTS: Decoupling Planning from Execution via Training-Free Atomic Experience Retrieval
LLM-powered systems require complex multi-step decision-making abilities to solve real-world tasks, yet current planning approaches face a trade-off between the high latency of inference-time search and the limited generalization of supervised fine-tuning. To address this limitation, we introduce \textbf{SGA-MCTS}, a framework that casts LLM planning as non-parametric retrieval. Offline, we leverage Monte Carlo Tree Search (MCTS) to explore the solution space and distill high-fidelity trajectories into State-Goal-Action (SGA) atoms. These atoms are de-lexicalized primitives that abstract concrete entities into symbolic slots, preserving reusable causal logic while discarding domain-specif...
| Authors | Xin Xie, Dongyun Xue, Wuguannan Yao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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