Papers with strategic exploration
Reasoning with Language Model is Planning with World Model (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown remarkable reasoning capabilities, particularly with Chain-of-Thought-style prompts. |
| Approach: | They propose a framework that repurposes the LLM as both a world model and a reasoning agent and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) |
| Outcome: | The proposed framework repurposes the LLM as both a world model and a reasoning agent and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) it achieves optimum balance between exploration and exploitation, while achieving high-reward reasoning paths efficiently. |
SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have enabled dynamic reasoning in automated data analytics, but rigid, single-path workflows restrict strategic exploration and often lead to suboptimal outcomes. |
| Approach: | a new framework replaces rigid workflows with adaptive, multi-path planning . the framework offers two operating modes: SPIO-S and SPIO -E . |
| Outcome: | a new framework outperforms state-of-the-art pipelines on Kaggle and OpenML benchmarks. |
AT²PO: Agentic Turn-based Policy Optimization via Tree Search (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have catalyzed the development of autonomous agents capable of executing complex, multi-turn tasks. |
| Approach: | They propose a framework for agentic reinforcement learning that integrates turn-level tree search with tree search to address key challenges. |
| Outcome: | The proposed framework addresses key challenges: limited exploration diversity, sparse credit assignment, and misaligned policy optimization. |