Why Your Long-Running AI Agent Drifts: The RL Instability Paper Worth Reading
Dev.to AI
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Generative AI
AI Research
Reinforcement Learning
If you've built a multi-turn AI agent and watched it degrade over long task chains, becoming repetitive, losing the thread, producing inconsistent outputs 20 turns in, you've probably blamed the context window, the system prompt, or the base model quality. There's a fundamental cause, and a January 2026 preprint describes it with enough precision to change how you think about the problem. The Paper AT²PO: Agentic Turn-based Policy Optimization via Tree Search identifies three structural failure modes in multi-turn agentic systems trained with reinforcement learning.