ReGE: Stronger Agents through Experience Synthesis

ReGE-Syn uses a single coach call to distill recorded execution traces and artifacts into self-contained guidance that a fresh attempt adapts to its own observations, with no parameter updates and no stronger teacher. It improves accuracy by an average of 9.9 percentage points for GPT and 8.8 for Claude across four benchmarks while cutting guidance-construction tokens by 98.2% and 95.5% respectively versus an adapted AggAgent baseline.