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New research from Tencent introduces Recursive Synthetic Terminal Tasks (RST), a method that progressively generates harder, verifiable training tasks for AI agents. Starting from 639 tasks, it produced 37,484 verified tasks across 15 rounds, and reinforcement learning with these tasks improved Qwen3.5-27B from 22.7% to 32.0% on Terminal-Bench Hard.
This paper demonstrates that data selection in low-resource verification regimes, where verifiers only have access to fragmented and biased slices of the target distribution, can paradoxically accelerate model collapse by pruning globally relevant tail modes. The authors provide theoretical proof and propose a collaborative proxy reference mechanism as a mitigation strategy.
SkillFlow proposes a flow-driven recursive skill evolution framework for LLM-based agentic orchestration, using Tempered Trajectory Balance to prevent strategy collapse and provide transparent credit assignment. Experiments on 14 datasets show significant improvements over baselines in QA, math, code, and decision-making tasks.