Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission. labs.baseten.co/

Introducing LangSmith Fine-Tuning and the smithtune CLI. LangSmith now handles the entire fine-tuning process. Use your traces to train specialized models that cut cost and latency. Now in Public Beta. langchain.com/blog/langsmith…

Stoked to launch 𝚜𝚖𝚒𝚝𝚑𝚝𝚞𝚗𝚎 in public beta! Post-training your agent's policy has never been easier, with training APIs from @baseten and @FireworksAI_HQ becoming increasingly accessible. But sourcing high-quality data to use them effectively remains a challenge. LangSmith processes hundreds of millions of traces a day, making it a rich system of record for your agent’s behavior. 𝚜𝚖𝚒𝚝𝚑𝚝𝚞𝚗𝚎 turns those trajectories into the data you need for supervised fine-tuning, with an end-to-end pipeline for curation, preparation, training and deployment for models you want to SFT for specific tasks. We care deeply about giving teams ways to own their own intelligence, and as the open model frontier continues to advance, it’s becoming increasingly clear that post-training on your own agent data will become a key part of that. Excited to get this into people's hands!

We believe openness to be an advantage for AI safety. Openness provides more visibility into the behavior of models and, most importantly, greater means of turning safety research into actionable and transparent controls than closed-source. This is why Baseten and Base Labs are building a stronger safety and security standard for open models, with the launch of our safety infrastructure. Base Labs will develop and publish methods for training and monitoring open models, and Baseten will integrate that work into its deployment infrastructure, live at runtime, and offer this work as a managed service. This will be a standard that is transparent and built into how our models are trained and deployed. We invite the open-source community to contribute, and are proud to partner with @huggingface and @GoodfireAI to bring this vision to fruition. Together, we are building an ecosystem of open models that are safe and accessible to all.

We believe openness to be an advantage for AI safety. Openness provides more visibility into the behavior of models and, most importantly, greater means of turning safety research into actionable and transparent controls than closed-source. This is why Baseten and Base Labs are building a stronger safety and security standard for open models, with the launch of our safety infrastructure. Base Labs will develop and publish methods for training and monitoring open models, and Baseten will integrate that work into its deployment infrastructure, live at runtime, and offer this work as a managed service. This will be a standard that is transparent and built into how our models are trained and deployed. We invite the open-source community to contribute, and are proud to partner with @huggingface and @GoodfireAI to bring this vision to fruition. Together, we are building an ecosystem of open models that are safe and accessible to all.

New episode with @johnschulman2, @oneill_c and @BerenMillidge. I got together with some of the most insightful AI researchers I know who are at the openish companies, because I wanted to hear the details of what's actually happening at the frontier and what comes next. 0:00:00 – Steelmanning the case against RSI 0:18:39 – What’s driving the Chinese labs’ progress 0:28:06 – How will automated AI researchers be trained 0:33:51 – Will long-horizon RL elicit AGI? 0:45:24 – The sim-to-real gap 1:00:33 – How much progress is explained by data? 1:18:03 – Why is RL working so well? 1:24:54 – Move 37 and entropy collapse 1:28:31 – Rapid-fire timelines

New episode with @johnschulman2, @oneill_c and @BerenMillidge. I got together with some of the most insightful AI researchers I know who are at the openish companies, because I wanted to hear the details of what's actually happening at the frontier and what comes next. 0:00:00 – Steelmanning the case against RSI 0:18:39 – What’s driving the Chinese labs’ progress 0:28:06 – How will automated AI researchers be trained 0:33:51 – Will long-horizon RL elicit AGI? 0:45:24 – The sim-to-real gap 1:00:33 – How much progress is explained by data? 1:18:03 – Why is RL working so well? 1:24:54 – Move 37 and entropy collapse 1:28:31 – Rapid-fire timelines

Sub 40s delta weight syncs for GLM 5.3 🔥💚

We partnered with @baseten to post-train recursive language model (RLM) agents for M&A diligence. We found that model-harness co-optimization meaningfully improves agent performance in long-horizon environments, bringing us closer to agents that can complete M&A diligence end-to-end. We started by building an RLM harness that lets a root agent search a data room, delegate document review to sub-agents, and orchestrate the sub-agents' work to produce a final diligence memo. By distributing document review across sub-agents, the RLM harness can work through thousands of documents in data rooms containing up to 80M tokens. On LAB Diligence, our synthetic M&A diligence benchmark, the RLM harness raised average rubric criteria pass rate across models from 23% to 62%. Despite the root agent processing just 1-4% of the tokens in diligence (with sub-agents processing 96-99%), the choice of root agent had a much larger effect on performance than choice of sub-agents. This led us to focus on the root agent, training two models separately to improve diligence performance. First, we trained a GLM-5.2 root model in the RLM harness using self-distillation SFT. We selected GLM-5.2 diligence runs with high data room coverage and fine-tuned the model on these trajectories. This increased benchmark performance from 46% to 60%. Second, we trained a base Qwen3.5-122B-A10B using GRPO within the RLM harness, keeping the sub-agent models fixed. This raised rubric criteria pass rate from 30% to 63% on 50 held-out data rooms. In both cases we saw major qualitative shifts in agent behavior. The trained root agent learned to delegate more thoroughly and write its memo incrementally as sub-agent findings came back. Data room coverage rose from 62% to 96%. And the root agent learned to scale the size of its agent team proportionally to the size of the data room. We are now scaling RL training with GLM-5.3, a frontier open-weight model, as the root agent. A small amount of training gives an increase in pass rate from 51% to 59%. @nikogrupen walks through these experiments with more on harness design, training setup and results, and what we're exploring next:

Today we're launching Base Labs, a research lab by Baseten. Our mandate is to make open-source as useful as possible, and our one rule is that we publish without exception, including what fails. Up until pretty recently I thought the way to get the world onto open models was to train them for one company at a time. @mudithj, @maxkirkby and I cofounded @parsedlabs on that bet, @baseten acquired us, and we spent the last year running their training team doing it for customers one by one. Every one of those engagements taught us something new about how models learn, forget, specialise and get cheaper, and almost none of it got spoken about. Sadly, in general that's the field's default in that the people who know the most about training have the least freedom to say it. To be clear I don't think the closed labs are the villains here. They get to new capabilities first, which buys the rest of us time to harden the world before that stuff is everywhere, and they're the ones paying to find out what's actually possible. But I'm fairly convinced the only real advantage they have is data and scale, and their incentives point squarely at the frontier. You can't do slow, public science on how these things learn when your job is the best model by end of quarter. Someone without that pressure has to, and there are very few of those someones around. Hence Base Labs. We have the broad remit of making open-source as useful as possible and our one rule is that we publish without exception, including what fails. The first problem is continual learning, which I have come to think is several problems wearing one name. We are also working on the open RL environments and data that open models need and currently can't get, because we have to aggregate data with the same ferocity everyone's been talking about aggregating compute. Plus a bunch of other stuff I'm genuinely excited about, eg a safety stack people can run on top of open deployments, and performance research so these things are cheap for everyone to serve. I still think open and closed coexist, and that's the good world. It's just that coexistence isn't free, someone has to actually do the work, and this is basically what keeps me up at night. We're hiring researchers, engineers and fellows. Come help distribute the mandate of heaven!

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission. labs.baseten.co/

Post-train GLM-5.3 and GLM-5.3-Flash on Baseten Loops. Inference + training support on day 0. docs.baseten.co/loops/overvi…

BaseTen is a fountain of good information, they're giving a lot and contributing to open source and research. Highly recommend spending some time reading their work if you want to level yourself up.