Bullish on this trend of making post-training more accessible. A new post-training era is upon us. If you work on agentic RL, long-context tasks (a big focus today) are expensive, inefficient, and don't scale well. I've been diving into RL envs and evals for long-context tasks, and I can see this being useful. In agent RL, rollouts use most of the tokens. Every turn re-reads the whole growing context, including tool outputs, files, and earlier turns. Tinker just cut the price of those tokens. Long-context prefill and sampling now cost the same as short context. This means that evaluating your trained models on long inputs also gets cheaper. Huge win here. I believe RL will keep unlocking specialized models that slash the cost of critical agent operations. Cheaper long rollouts make them more practical to build. Own your intelligence stack!