Terminal-RSI：通过递归自改写与策略精炼实现递归自我提升的终端智能体

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Abstract:Converting verifier outcomes into useful policy updates and partial progress into complete task resolution is crucial for terminal agents. We present Terminal-RSI, which alternates Recursive Self-Rewrite (RSR) of verified trajectories with critic-inheriting Recursive Policy-Refine (RPR). We formulate Turn-Boundary Skip-Observation GAE (TB-GAE) to retain token-specific baselines while applying decay across interaction turns. Family Dense Reward (FDR) calibrates semantic progress across task families; Success-Prioritized Reward (SPR) separates strict success from bounded partial credit. A Warm-up Critic Model, recursive critic handoff, and stage-specific learning rates coordinate the two refinement phases. The five-stage sequence from Qwen3.8-27B base model through RSR-1, RPR-1, RSR-2, and RPR-2 yields Terminal-RSI-27B: 19.84% on Terminal-Bench 4.0, 86.52% on Terminal-Bench 2.1, 16.95% on Terminal-Bench 3.0, and 79.00% on Terminal-Bench Hard. On LHTB, process reward rises from 0.21 to 0.37 and strict completion from 0/46 to 5/46; SWR100 success doubles from 3.00% to 6.00%. By recursively coupling verified trajectory rewriting with critic-inheriting policy refinement, each retained stage consolidates discovered behaviors and progressively advances the agent from partial local repairs to strict terminal verification.

Comments:	29 pages, 16 figures, 5 tables
Subjects:	Artificial Intelligence (cs.AI)
Cite as:	arXiv:2610.22192 [cs.AI]
	(or arXiv:2610.22192v2 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2610.22192 arXiv-issued DOI via DataCite

Submission history

From: Junyao Yang [view email]
[v1] Thu, 15 Oct 2026 16:43:56 UTC (11,576 KB)
[v2] Sun, 25 Oct 2026 06:56:48 UTC (11,576 KB)