Cognition released SWE-1.7 yesterday — using a large-scale asynchronous reinforcement learning round on the already-deeply-RL-post-trained Kimi K2.7 base, pushing the open-source programming model to the same tier as GPT-5.5 and Opus 4.8, with cost only one-tenth of closed-source competitors. On benchmarks, SWE-1.7 takes 42.3% on FrontierCode 1.1 Main, 12 percentage points above Kimi K2.7 Code, almost catching GPT-5.5, and only 4 points behind Opus 4.8; about $1.97 per task, served in real-time on Devin at 1000 TPS through Cerebras. The training engineering is the highlight. Cognition uses four core innovations to make "post-training can still gain 12 points" hold: top-p sampling replay flattens the entropy collapse from long RL; cross-three-continent multi-cluster RL lets a 1T-parameter model update across continents in just 1-2 minutes; self-compaction + alternating length penalty stretches a single rollout to 6 hours; high-quality verifier data pipeline filters low-signal samples, with cheats always rewarded 0. SWE-1.7 slaps the pessimistic narrative of "base RL has been squeezed dry" — the same K2.7 in Cognition's hands gains another 12 points, showing the RL post-training ceiling is far from reached; the "cross-continent distributed RL + Cerebras 1000 TPS inference" engineering combination also provides a replicable sample for small and medium teams to "train a frontier programming model".