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Research questionHow can terminal-agent training environments stay challenging as models improve without costly on-policy synthesis?Scratch-generated environments can provide weak learning signals once agents become capable of solving them. On-policy synthesis requires repeated rollouts and may not generalize well beyond the observed training situations.
AI
AI Agents
Evaluation & Benchmarks
Machine Learning
Reinforcement Learning
Latest papersRecent research connected to this question, newest first.Environment Evolution for Terminal AgentsThe source studies off-policy evolution of terminal environments through a multi-agent harness, increasing difficulty across training generations. Evidence comes from rollout experiments with several frontier models and long-horizon reinforcement-learning results for two Qwen models on Terminal-Bench 2.1; broader deployment and generalization are not established.research paper · Sep 3, 2026
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