Apple's RLTL;DR: agents self-improve by writing feedback after failures
Apple ML Research · rss · 2026-10-01
Apple ML Research introduces RLTL;DR, addressing a key RLVR limitation in self-improvement: when tasks are too hard for the agent to succeed and no teacher models or example solutions exist, optimizing toward successful rollouts breaks down. The method shows the policy the verifier's output after each failed attempt, lets it write a single TL;DR insight as its own feedback, and conditions the next rollout on it, enabling learning internalized from failures.
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