Kimi K2.7 gains 12.5 points from 1,700 tasks

Surge AI says reinforcement-learning post-training on 1,700 coding tasks lifted Kimi K2.7 by an average 12.5 percentage points across five external benchmarks, with DeepSWE trajectories shrinking from 150 to 98 steps, according to the study and training configuration. The gains held across three agent harnesses and included tasks not targeted in training. Builders should file this as evidence that curated task experience—not only larger pretraining—can improve agent reliability, then validate on their own repositories alongside Ai2’s benchmark-quality warning.