Beyond Policy Optimization: A Data Curation Flywheel for Sparse-Reward Long-Horizon Planning

• URL: http://arxiv.org/abs/2508.03018v1
• Date: Tue, 05 Aug 2025 02:56:58 GMT
• Title: Beyond Policy Optimization: A Data Curation Flywheel for Sparse-Reward Long-Horizon Planning
• Authors: Yutong Wang, Pengliang Ji, Kaixin Li, Baolong Bi, Tao Feng, Guillaume Sartoretti,
• Abstract summary: We propose a three-stage framework to develop robust reasoning models for sparse environments.<n>Our framework bootstraps efficient reasoning using the proposed planning quaternions with long-short chain-of-thought fusion.<n>Experiments on ALFWorld, ScienceWorld, and WebShop demonstrate that our approach achieves state-of-the-art with significant token efficiency.
• Score: 15.103861901247125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Reasoning Models have demonstrated remarkable success on static tasks, yet their application to multi-round agentic planning in interactive environments faces two fundamental challenges. First, the intractable credit assignment problem renders conventional reinforcement learning ineffective in sparse-reward settings. Second, the computational overhead of verbose, step-by-step reasoning histories is prohibitive. To address these challenges, we propose BPO, a three-stage framework (bootstrapping, extrapolation, and refinement) that establishes a self-improving data flywheel to develop robust reasoning models for long-horizon, sparse-reward environments. Our framework first bootstraps efficient reasoning using the proposed planning quaternions with long-short chain-of-thought fusion. It then extrapolates to out-of-distribution tasks through complexity-stratified curriculum learning. Finally, the model iteratively refines itself by learning exclusively on experiences selected via reward-gated rejection sampling. Experiments on ALFWorld, ScienceWorld, and WebShop demonstrate that our approach achieves state-of-the-art with significant token efficiency, providing a new recipe for reasoning models in agentic planning.

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