dailyai.report

Teacher models fail 57% of trials on the τ 2-bench, wasting costly compute during tool-calling distillation. Apple introduced PROOF-Gen to recover these failures by optimizing data trajectories instead of simply filtering them. This approach converts near-misses into training signals. Practitioners can now reduce teacher-model costs while improving agent reliability in hard scenarios.