Tinker

RL Systems Mind the Gap: Matching Trainer and Generator Throughput RL Training Infrastructure, GRPO, PipelineRL, Async RL, Policy Staleness, RL Sandbox Infra, CPU Requirements, TCO Analysis, Thinking Machines Tinker newsletter.semianalysis.com/p/rl-systems-m…

We have been exploring new algorithmic frontiers and are excited to share our contributions to Self Distillation Policy Optimization (SDPO) for agentic continual learning, check out our blog post here: trajectory.ai/field-notes/sc…

Today, @MichaelElabd, @QuantumArjun, and I are excited to announce Trajectory. We are a research lab and product company building the platform for Continual Learning. Our platform unlocks the signal already sitting in product usage, so companies can continuously post-train large-scale agentic models that outperform the frontier. @trajectorylabs We’ve raised $15M from @Conviction, @BessemerVP, @radicalvcfund, @jeffdean, @drfeifei and more. We’re partnering with some of the best AI-native companies: @ClayRunHQ @Harvey, @DecagonAI, @mercor_ai, @RogoAI to power their agentic systems, some of which we are already in production with. We’ve brought together a world class research team from DeepMind, OpenAI, Apple, Meta Superintelligence, Amazon AGI, Scale AI, and an elite product team from Stripe and Figma. AI will never again start on day one. Every correction, every retry, every edit will make products smarter. This is Continual Learning.

People talk, listen, watch, think, and collaborate at the same time, in real time. We've designed an AI that works with people the same way. We share our approach, early results, and a quick look at our model in action. thinkingmachines.ai/blog/interacti…

New preprint from @lightningrodai! We trained AI to predict clinical events — ICU transfers, new diagnoses, complications, procedures, ventilation, mortality — directly from raw clinical notes. No labeled data required – Foresight Learning infers outcomes from what happens later in patient records. Using Tinker from @thinkymachines , we trained a lightweight adapter on GPT-OSS-120B, resulting in a specialized predictor that runs on a single GPU. Results: 🎯 ~70% lower calibration error 📈 Brier skill score: ~0% → 27% 🧠 84% win-rate vs the base model in blind reasoning review 🥇 Slightly better Brier than GPT-5, despite being a fraction of the size Hospitals and specialty clinics often treat unique patient populations that out-of-the-box models don't have training data for. This makes it possible to build frontier-quality predictors for highly specific patient groups, with nothing but raw clinical records. Congrats to the team — @indiequant @KSkotheim64001 🙌 Full paper 👇 arxiv.org/abs/2605.12817

How does Exa compare to Google for training LLMs to search? In this blog post, we find that LLMs using Exa during reinforcement learning reach higher performance with 70% less training compute. exa.ai/blog/rl-search…

the @tinkerapi tutorials were really well put together thank you @thinkymachines folks this was really helpful for the project I'm working on

FrontierSWE was built with collaborators from industry and academia to ensure that tasks are diverse and reflect real work engineers and researchers encounter. We specifically thank our partners @Modular, @PrimeIntellect and @thoughtfullab for their contributions

Another task tests AI research capabilities: using @tinkerapi from @thinkymachines, agents are asked to post-train an agent to play logic games, which involves writing an entire training pipeline and running experiments with different recipes to finally submit the best model