On-Policy Self-Evolution via Failure Trajectories for Agentic Safety Alignment

Tool-using LLM agents fail through trajectories rather than only final responses, as they may execute unsafe tool calls, follow injected instructions, comply with harmful requests, or over-refuse benign tasks despite producing a seemingly safe answer. Existing safety-alignment signals are largely response-level or off-policy, and often incur a safety-utility trade-off: improving agent safety comes at the cost of degraded task performance. Such sparse and single-objective rewards severely limit r

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ethics.ai (12 May 2026), “On-Policy Self-Evolution via Failure Trajectories for Agentic Safety Alignment,” evidence record 4481, https://ethics.ai/record/4481 (originally published by arXiv).

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