How to post-train a model to personalize it on your code repo? In our latest research in Bespoke Labs, we post-trained a model to improve its performance on a given Github repository. Starting from Inkling base, we use supervised fine-tuning (SFT) with trajectories coming from a strong teacher model, and reinforcement learning (GRPO) on repository-specialized environments that we curated. SFT gave a 52pp improvement in performance on the held-out fontTools evaluation set. Further RL training lifts the total improvement to 57pp compared to the base Inkling model. In addition to the in-distribution evaluation our post-trained Inkling shows good performance on Terminal-Bench 2.1 and SWE-Bench Lite while becoming 40% more token efficient due to post-training. Read our full research blog post here: Many thanks to Thinking Machines Lab for their credit contribution that helped support this research.