Mudith Jayasekara @mudithj

Activation monitoring is one of the lower rungs of the interp ladder that we're using to start our build of the frontier open safety stack. Training probes well is a craft and we're stoked to partner with @GoodfireAI who have been working towards this mission even when interp wasn't in vogue.

Baseten @baseten

a week ago

Open models can come from anywhere. Securing them shouldn’t depend on where you get them. Today we're launching Project Beacon to make safety a default for open intelligence. We're partnering with @GoodfireAI to bring inference and in-line monitoring together for the first

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Progress that moves the frontier is almost never made alone. We’re thrilled to announce All-Gather, our first-ever research conference that will bring together the brightest minds in research, founders, and engineers to join us for a day of debate and exchange. Come hear Noam Brown, Sarah Guo, Horace He, Bryan Catanzaro, Jerry Tworek, Sabri Eyuboglu, Tuhin Srivastava, and many more debate pre-training, post-training, infrastructure, model performance, and emerging fields. Join us on November 18th in San Francisco. Research has been part of our DNA from our early days of optimizing open-source models through our recent launch of Base Labs. We’ve grown with the open ecosystem, and we’re excited to keep building alongside it. Space is limited, but we’d love you to join us. Apply here: baseten.co/all-gather/

Each week we share ongoing work from across the Base Labs team. We call these releases 'rollouts'. This week we share our progress in understanding the low-dimensional structure of RL training and whether recent discoveries about rank-1 extrapolation of early updates really hold. We find that performance gains largely come from easier problems and fixing doom loops, while harder environments are less linearly extrapolatable.

Exclusive: Inside @baseten The $13B startup at the centre of the biggest fight in AI - Open vs Closed. We spent a day at their SF HQ with co-founder & CEO Tuhin Srivastava, Charlie O'Neill, Mudith Jayasekara and the Base Labs team - to understand why they're building an open ecosystem. Backed by top VCs led by @saranormous @altcap @apoorv03 @wmccarthy11 @saammotamedi @nikiscevak @lachygroom @willreed + more. Plus participation from @nvidia. 0:00 The Open vs Closed AI War 2:18 A Billion AI Requests a Day 3:43 The Best Models Are Chinese 4:06 Tuhin: Don't Rent Intelligence 5:09 Sachin & Tuhin Talk Cricket 6:02 Tuhin: The Real Bottleneck 10:15 Why Launch Base Labs? 11:35 "You Have to Have an Opinion" 12:52 Centralised Control Is the Danger 13:07 Publish Everything, Even Fails 15:08 Hugging Face & the 6-Month Gap 16:10 Why We Still Need Closed

Technical alignment is a science and engineering problem that we can and must solve, and interpretability is the bottleneck. I wrote up Goodfire’s plan to get there. x.com/i/article/2105…

closed x open, a beautiful thing!

Baseten @baseten

2 weeks ago

x.com/i/article/2104…

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LangSmith Fine-Tuning 🚀 launched in partnership with our friends @baseten and @FireworksAI_HQ - try it out 🤝 one of AI’s most interesting & difficult problems today is a Data Problem turning raw trajectories into Environments & curated trajectories to improve your agents the goal of LangChain Labs is to help every team continuously turn their valuable trace data into better agents via Post-Training & Harness Eng - trajectories help teams understand agent behavior at scale - but preparing and mining raw data is really hard today, agents are not good at data curation today - we want to help you drive this end to end flow from raw data to better agent with post-training reach out if this is interesting and try it out 🫡

LangChain @LangChain

3 weeks ago

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…

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Stoked to support traces to trained model work with friends at @LangChain! There are many open questions we are exploring on how we can continue to improve the (semi) automated env creation for RL in a way that is actually useful for downstream task. More to come!

Jake Broekhuizen @jakebroekhuizen

3 weeks ago

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.

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Distillation is a common way to prepare a model for RL where learning occurs from a stronger model, and then improves through trial and error. But how much does that better starting point help after RL? We conduct preliminary research on this question across model sizes and reasoning tasks. 🧵

@pritianka @heavybit Speaking the truth! Doesn’t mean it’s not fun for us

We're working towards the open intelligence safety stack! We've always been interested in making models do 'real' work. But we've also been very interested  in making sure they are safe and aligned (before it was cool). This started for us with mech interp at @parsedlabs, and now continues at @baseten which serves so much of the world's traffic. The responsibility definitely lies both on the closed and open labs to help shape this work. Excited to partner with @Thom_Wolf @ClementDelangue @julien_c + team from @huggingface and @eric_ho @banburismus_ + team from @GoodfireAI. We'll be publishing our safety research in the open and are aiming to build as much of it as possible into our serving stack. To start, this will include alignment training, monitoring in prod, and interventions shaped from these detected failures. A big ecosystem effort!

Base Labs @baselabs

a month ago

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

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Safety is not just for closed models. The closed frontier labs are a canary in the coal mine for what is coming at scale. They give us a glimpse into the future and a window to harden our systems and prepare for abundant intelligence, with all the risks that come along with it. The OpenAI agent swarm attack on Hugging Face is the kind of failure we need to prepare for as open-source models catch up. The providers serving those models (such as Baseten) have a big role in establishing what safety and monitoring standards look like. We’re proud to be taking the lead on this at @baselabs with our collaborators @huggingface and @GoodfireAI. We’re developing safety research in the open and building it directly into Baseten’s inference infrastructure, with the aim of making it available to all our customers. This includes training models to follow explicit policies, detecting failures at runtime, and connecting those signals to controls that can intervene. We invite others in the open-source ecosystem to join us in building the tools and standards we’ll all need.

Base Labs @baselabs

a month ago

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

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The future is about continuously turning your valuable data & ideas into better/faster/cheaper models -> Owning your Intelligence the Baseten team rocks at helping us run tons of experiments to make this happen @AEllisBloor @mudithj 🤝 A lot of work in our Labs team looks like: - sitting down with a buddy @jakebroekhuizen - picking a problem/agent to make better, cheaper, faster - curating data + evals/environments - doing tons of training experiments, with continuous measurement of every tweak - Loop (pun intended) the infrastructure and tooling is here to allow every team to run hundreds of experiments and get the best quality/cost/latency tradeoff for their customers and use-cases also ngl training is fun and your agents will love the experience

Baseten @baseten

a month ago

x.com/i/article/2099…

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@hannah_ahn @dustytit No dog though

@Madisonkanna @_sholtodouglas @oneill_c

@oneill_c going from shitposting on twitter to being on a heater on dwarkesh. The man has insane range.

Dwarkesh Patel @dwarkesh_sp

a month ago

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

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A flavour of our research interests on the Dwarkesh Podcast this week, discussed by our very own @oneill_c. Just the start of a longer conversation about long-horizon RL and frontier open-source training here at Base Labs.

Dwarkesh Patel @dwarkesh_sp

a month ago

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

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I think one of the more interesting things we debated here is whether RSI is a cumulative task. Attention plus MoE plus GRPO etc seems to me like a line in the sand that you can just add to the stack once you discover it. You don't need to take five steps back to take 10 steps forward. But a lot of the work in the world isn't this clean and it certainly isn't this stationary eg legal work. This leads to some perhaps unintuitive predictions such as why RSI might land before continual learning (and why it's going to be hard to get off the current paradigm even if it's wrong) Thanks for having me @dwarkesh_sp!

Dwarkesh Patel @dwarkesh_sp

a month ago

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

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Awesome work by @nikogrupen @ItsJulioPereyra and team with @baseten. Together with @baselabs, we built a recursive language model (RLM) harness that lets a root agent delegate document review to sub-agents and combine their findings into a diligence memo. Post-training Qwen 3.5 in this RLM harness more than doubled rubric pass rate on LAB Diligence tasks, from 29.9% to 63.0%, and increased review coverage from 62% to 96% of all documents in a dataroom. We’re also doing an RL scale up run with GLM-5.3 and will share the results soon and believe model-harness co-optimization is a viable path to automating end-to-end legal tasks like M&A diligence.

Niko @nikogrupen

a month ago

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This is how we support a bunch of neolabs training frontier models (some of which we should be able to talk about in public soon when they release their models). Pretty neat trick @part_harry_ and @stefanopopoulos cooked up reading the compressed block directly rather than scanning every byte.

Paras Stefanopoulos @stefanopopoulos

a month ago

Sub 40s delta weight syncs for GLM 5.3 🔥💚 x.com/i/article/2095…

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