RLTL;DR method lets models self-improve from self-written insights
A paper introduces RLTL;DR, which shows a policy the verifier output after each failed attempt and lets it write a single TL;DR insight. Conditioning rollouts on prior insights and backpropagating on them raised Pass@1 from 0-1% to 14-31% on tool-calling and coding datasets, with 12-13% at eval without insights.
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