On-policy distillation would revolutionize multi-turn tool-use training beyond RL, but neither Tinker nor TRL which implements on-policy supports anything other than single-turn distillation. We therefore have taken this upon ourselves and implemented this feature in native Tinker. Specifically, with a trainable Tinker client, a model can now call a list of tools, interact with tool results for multiple turns, and return tokens, logprobs, and reward masks sufficient for a distillation training job (p1-2). The engineering we have achieved is to implement tool calling and parsing for Tinker models, which lies in @thinkymachines 's TODO list in their tinker_cookbook code (p3). Apart from that, we also create a dedicated inference stream that spins up robust, multi-turn tool loop that can run alongside a training job and sync the weights in real time. It becomes easy to write a simple training loop with KL loss to run on-policy distillation with tool use. This opens the door for a new domain of application in agentic LLM because small/medium models now have access to dense, on-policy rewards from a swarm of SOTA large models (deepseek, gpt-oss). We will next up begin our training runs and see how they compare with traditional RL/SFT on multi-turn tool use.