StraTA: Incentivizing Agentic Reinforcement Learning with Strategic Trajectory Abstraction
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| Authors | Xiangyuan Xue et al. |
| Year | 2026 |
| HF Upvotes | 19 |
| arXiv | 2605.06642 |
| Download | |
| Code | https://github.com/xxyQwQ/StraTA |
Abstract
Large language models (LLMs) are increasingly used as interactive agents, but optimizing them for long-horizon decision making remains difficult because current methods are largely purely reactive, which weakens both exploration and credit assignment over extended trajectories. In this work, we present Strategic Trajectory Abstraction (StraTA), a simple framework that introduces an explicit trajectory-level strategy into agentic reinforcement learning (RL). StraTA samples a compact strategy from the initial task state, conditions subsequent actions on that strategy, and trains strategy generation and action execution jointly with a hierarchical GRPO-style rollout design, further enhanced by diverse strategy rollout and critical self-judgment. Experiments on ALFWorld, WebShop, and SciWorld show that StraTA consistently improves both sample efficiency and final performance over strong baselines. StraTA reaches success rates of 93.1% on ALFWorld and 84.2% on WebShop. On SciWorld, StraTA attains a 63.5% overall score, outperforming frontier closed-source models.
Engineering Breakdown
Plain English
This paper presents StraTA, a framework that improves how LLM-based agents make long-horizon decisions by having them first sample a high-level strategy from the task state, then condition all subsequent actions on that strategy. The key innovation is training strategy generation and action execution jointly using a hierarchical GRPO-style approach with diverse strategy rollouts and self-judgment, tested on interactive environments like ALFWorld, WebShop, and SciWorld.
Key Engineering Insight
The core insight is that breaking down long-horizon RL into two levels—sampling a compact strategy upfront, then executing actions conditioned on that strategy—dramatically improves credit assignment and exploration compared to purely reactive policies. This hierarchical abstraction lets the agent reason about intent before diving into step-by-step execution.
Why It Matters for Engineers
Current LLM agents struggle with multi-step decision making because they're trained reactively, step-by-step, making it hard to assign credit and explore effectively over 50+ steps. StraTA addresses this by forcing the agent to commit to a strategy first, which acts as a stabilizing constraint that guides exploration and makes reward signals clearer—directly relevant if you're building production agents for web automation, planning, or complex task decomposition.
Research Context
Prior work on agentic RL with LLMs has focused on either end-to-end reactive policies or brittle hierarchical planning. StraTA advances the field by showing that a lightweight, learned trajectory abstraction (strategies) integrated into the RL loop outperforms both approaches. This opens the door to better long-horizon reasoning in interactive environments without requiring hand-engineered hierarchies.
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