Tinker降低长上下文训练成本
Tinker大幅降价，长上下文训练成本降低70%，让智能体RL更实用了
Tinker公司将长上下文预填充和采样成本降至与短上下文相同。该公司对GLM-5.3-Flash和DeepSeek-v4.1-Flash模型进行了优化，支持长上下文强化学习。价格降幅最高达70%，使智能体RL中的长上下文任务评估成本大幅降低。
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! Tinker @tinkerapi Tinkerers have been busy scaling up long-context RL! We’ve made significant improvements to Tinker’s efficiency to support those, and are passing these on with price cuts up to 70%. GLM-5.3-Flash and DeepSeek-v4.1-Flash are also live for cost-efficient long-context work. 🔗 View Quoted Tweet 💬 10 🔄 2 ❤️ 16 👀 1868 📊 9 ⚡
