Behrooz Ghorbani

Leading Reinforcement Learning at @Reflection_ai. Scaling RL for frontier reasoning models. Formerly @OpenAI, @GoogleBrain and @stanford_ee.

[Jensen Huang @JensenHuang]

For my first post, I’m sharing a letter @NVIDIA signed on why open models matter.

AI will transform every industry, power every company, and be built by every country.

Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty.

The world needs both frontier closed models and frontier open models.

https://t.co/AUKzoQ5Ikb

[Reflection @reflection_ai]

Beam’s model technical report is now available. Read the full report to learn about the methodology our team used to train Beam end-to-end in-house, here: https://t.co/wqc0nOyqhC

[Reflection @reflection_ai]

Sharing more about how we trained and evaluated Beam, our first model, for safety: https://t.co/FZIbzeUWav

In the coming weeks, we'll release additional safety research, evaluations, and tools under Project Iris, Reflection's open AI safety initiative.

We are committed to prioritizing safety in every model we build—and sharing what we develop and learn so that researchers, developers, policymakers, and enterprises can build on our work, advancing safety across the broader AI ecosystem.

[Reflection @reflection_ai]

In this demo, Beam built a real-time weather satellite tracker with Mirror, our open source harness, using publicly available orbital data. It fetches live positions, tracks coverage, and renders orbits on a 3D globe.

[Behnam Neyshabur @bneyshabur]

Introducing Fern, Mirendil’s first model: pretrained from scratch, with up to 10M tokens of context!

That’s ~0.5M lines of code (roughly Megatron-LM scale) or days of an agent’s work, all in context at once.

Its first version, Fern 0.1, had a short round of post-training 🚢

[Stephanie Zhan @stephzhan]

I’m starting a new early-stage AI fund, doing what I love: partnering with founders from company formation on bold bets, long before consensus.

The fund’s LPs will include Sequoia, Jeff Dean, Sachin Katti, Philip Wong (my Stanford professor and Chief Scientist adviser to TSMC), and others whose work has shaped our industry and how I think about AI’s future.

I’ll remain a partner at Sequoia and keep serving on my boards on Sequoia’s behalf.

Sequoia has been my home for the past 11 years. I joined as the youngest investor the firm had ever hired. I grew up here. Through years of intense debate and shared work, I found lifelong mentors and friends who took me under their wing and taught me this craft. They taught me to aim high and take big swings. I’ll carry that into what I build: outsized ambition, relentless truth-seeking and curiosity, and a refusal to become complacent.

Working alongside founders has made me want to build something of my own: a firm shaped from day one around the convictions I’ve developed over the past decade.

Life has taught me not to take time for granted. This is how I want to spend mine.

Why now

We’re seeing a new way of building companies in today’s AI era. Research and products often advance together. A breakthrough can open a market or accelerate a business far faster than expected. Some companies require extraordinary capital and compute early on. Others reach meaningful scale with remarkably small teams. The rules for how big these businesses can get, and how fast, are being rewritten.

But early on, the technology is unproven, the resources required enormous, and the vision seemingly implausible.

This is what I love most: seeing the potential right from the beginning, and helping founders realize their full scale of ambition.

More than ever, it starts with an extreme willingness to believe early, with bold, independent conviction in a founder’s technical insight before any product or market validation. Then, it means helping founders take the biggest swings, giving the rare, long-term patience to see those bets through uncertainty, ambiguity, and turbulence, and serving as a level head when the stakes are highest.

I learned this kind of partnership at Sequoia. It’s the foundation of the firm I’m building.

Believing before consensus

My fascination with AI’s potential took root at Stanford in the early-to-mid 2010s, studying computer science and working in Andrew Ng’s lab. The friendships I formed connected me to a community whose people and ideas would help shape modern AI and build some of our generation’s defining companies. What stayed with me was the gap between what those people saw was possible and what the wider world could imagine.

I’m drawn to founders pursuing ambitions that sound impossible today.

I start with a simple question: “What if everything goes right?”

Then we work backward together to make it happen.

In 2023, Deepak and Abhinav set out to build a general-purpose robot brain at @SkildAI, before physical AI was a consensus bet. Their sim-to-real breakthroughs gave me conviction where earlier robotics approaches had fallen short. But the data flywheel required deployment at scale. We made commercialization as urgent as research and brought in strategic partners like NVIDIA and SoftBank to support the ambition. Three years in, Skild is advancing the frontier of physical AI with nine figures in annualized revenue.

In 2024, Ioannis and Misha founded @reflection_ai with an insight into scaling reinforcement learning. As that grew into the ambition to build a Western frontier lab for open models, the hard question was how a startup competes with labs spending tens of billions. An introduction to NVIDIA and many compute calls and recruiting events later, the team has built Beam, now one of the leading US open models.

In 2025, Anna and Azalia, the co-creators of AlphaChip, founded @RicursiveAI to design models and hardware together. From day one, we made early customers and ecosystem partners a priority. Today, they are showing order-of-magnitude improvements on real workloads, working with some of the industry’s most consequential players, and reaching a defining inflection point.

The ambition keeps growing as these companies scale.

@linear has always had a kind of magic to it. Now it’s the system a new generation of AI companies builds on, for teams and agents alike.

@tavus saw early that we’d one day talk to AI the way we talk to each other. Today, it’s building the human computing interface for a world of AI colleagues.

@Mach_Industries began as hydrogen experiments in an MIT dorm room. It has since become one of the most exciting next-generation defense companies.

Making the ambition possible

Being a first believer is only the beginning. Compute falls through. A deep-pocketed incumbent makes your mission its top priority. A breakthrough changes the opportunity and the capital required overnight.

These are stomach-churning moments, and early belief carries a responsibility to help make the bet work.

This is the partnership I love: turning the full ambition into concrete steps. Raising capital, securing compute and strategic partners, recruiting exceptional people, and winning the first customers together. Thinking several moves ahead while solving what’s in front of us. And as breakthroughs open new possibilities, helping founders dream even bigger.

Thank you

To the founders I’ve worked with: thank you for trusting me and Sequoia to be your first partner and first believer. It’s been the joy of a lifetime, and I can’t wait to keep building together.

Thank you to Alfred and Pat for your leadership of Sequoia and your support of my next chapter. To Roelof, for bringing me into Sequoia and believing in me from the beginning. Alfred and Roelof, you have been mentors in investing and in life. So much of what I know, and the investor I’ve become, comes from your guidance and generosity over the years. To Bill, for the close partnership and wealth of knowledge, always wrapped in humility. To Shaun, for showing me how to think deeply and imaginatively. To Michael Moritz, for inspiring 22-year-old me to dream big. To Doug, for teaching me to keep things simple. To Bogie and Jess, for bringing joy to the everyday and perspective on what matters. And to all my Sequoia teammates, for the friendship, camaraderie, and lessons I’ll carry with me.

To my family, especially my husband and my brother, who have each started companies of their own: thank you for giving me the courage to take this leap.

Let’s get to work!

[Behrooz Ghorbani @_ghorbani]

Seeing capabilities like this emerge is one of the most exciting parts of pushing the frontier of RL.

[Behrooz Ghorbani @_ghorbani]

Really proud of Beam, our first-ever release! Among US open-weight models tested by Artificial Analysis, it ranks #2 on the Intelligence Index and #1 on the Cyber Index.

So grateful to the team for the work that went into this. We're just getting started 🚀

[Artificial Analysis @ArtificialAnlys]

Reflection’s first model, Beam, scores 30 on the Artificial Analysis Intelligence Index, placing it #2 among US open weights models

Beam is an open weights model with 501B total and 23B active parameters, released on @huggingface

Congratulations to @reflection_ai on the release!

Key takeaways:

➤ Second among US open weights models: Beam scores 30, behind only Meta's Muse Spark Open (39) and ahead of yesterday’s NVIDIA Nemotron 3.5 Super VL (26) release. Outside China, only Muse Spark Open and the UAE's K2 Horizon (31) score higher

➤ Token efficient for its intelligence: Beam uses ~26k output tokens per Intelligence Index task compared to K2 Horizon, one point ahead, which uses twice as many (~52k). Proprietary models still set the frontier, with GPT-6 Astra (max) scoring 53 on the same budget (~27k)

➤ Strong on agentic workflows, weaker in the terminal: Beam scores 48.5% on AutomationBench-AA, 11 percentage points ahead of K2 Horizon (37.2%) and level with Qwen3.8 27B (48.2%). Terminal-Bench 4.0 is a weak point, where Beam scores 3.0%, behind Qwen3.8 27B (5.6%) and Muse Spark Open (8.1%)

➤ Knows more, guesses more: On AA-Omniscience it answers 33% of questions correctly, well ahead of K2 Horizon (18%) and Qwen3.8 27B (16%). But its hallucination rate is 47%, against 26% for K2 Horizon and 30% for Qwen3.8 27B, giving Beam a net index score of 1.5, ahead of both peers (−3.0 and −10.0)

Other model details
Context window: 1M tokens
Modalities: Text
Parameters: 501B / 23B

[Jacob Loewenstein @spatialjlo]

Incredibly proud of @reflection_ai for immediately jumping into the #2 spot for US open models with their first ever model. Beam is SO fast and efficient for its size. Check it out

[Reflection @reflection_ai]

Beam is now available across platforms, with open weights under the Apache 2.0 license - in collaboration with an ecosystem of 40+ partners.

We're releasing:
- Weights, tokenizer, and reference implementation
- BF16, FP8 and NVFP4 weights and a DFlash2 drafter
- The open source harness we built and trained Beam on: Mirror CLI

Weights and model card on Hugging Face: https://t.co/tPqWpk9RXL

[Behrooz Ghorbani @_ghorbani]

@zkwentz Zach, it’s been an absolute pleasure working with you. Your leadership, dedication, and relentless drive have been instrumental in making this happen. I’m incredibly proud of what we’ve built together, and even more excited about what’s ahead. This is just the beginning 🚀🚀🚀

[Risto Vuorio @ristovuorio]

This was the first big run we've done at Reflection. We had a so much fun running a lot of RL on this one! It was very satisfying to see scaling RL in action with no plateau in sight!

[Alex Heath @alexeheath]

I really liked Reflection CEO @MishaLaskin's framing on the state of closed vs. open AI models:

"Closed models are the equivalent in real estate to renting an apartment... As AI adoption has increased, an ownership market is basically coming in.. The only way to own intelligence, by definition, is if it's open"

[Behrooz Ghorbani @_ghorbani]

@agbgarg Likewise, Ankush! So much fun getting to work together again after all these years. I’ve learned a ton from you too. Here’s to building more together! ❤️

[dylan ツ @demian_ai]

the chinese open weight frontier has been running without a serious western peer for a while, but today reflection dropped beam!

underneath there's a super solid team:

- ex-deepmind founders (@MishaLaskin / @real_ioannis)
- $4.6b raised,
- $25b mark, nvidia in the round.
- 23.8t pretrain on 6,144 GB300s
- then 10.5k gb300s for four weeks of rl
- 100m+ rollouts
- locked spacex colossus GB300 capacity
- locked $1b $NBIS compute deal (we announced this few months ago, letsgoooo)
- 501b total, 23b active, sparse MOE, trained from scratch, coding and agents first.

On their numbers it basically ties glm-5.2 on the agentic coding rows that matter for a workhorse:
- deepswe 44.4 vs 44
- terminal-bench 80 vs 81

but all this while using roughly 3 to 4x less inference compute for similar reasoning scores, quite cool!

it still trails glm-5.3 and deepseek v4.1 flash on those same benches tho (flash is a different animal anyway: tiny active count, multimodal). And yes kimi k3 stays ahead on raw capability.

So yes the western push is real, but part of product is efficiency (at least on this one)

closed frontier (opus / astra lvl) still owns the top of the capability curve but Beam isn’t pretending otherwise.

i guess it's fair if you compare open to open, and having a western model you can own that sits in glm 5.2’s neighborhood at a fraction of the reasoning compute, is quite impressive

as Laskin said: closed models are renting an apartment, an ownership market is forming. This is the first well-funded western open workhorse built for that

[Reflection @reflection_ai]

We scaled Beam with the largest publicly documented RL run we’re aware of, which ran on 10.5k GB300s for 4 weeks.

Algorithmic advances coupled with distributed infra enabled an RL system that scales.

Across our eval suite, capabilities continued to improve as we increased RL - with no signs of plateau.

[Artificial Analysis @ArtificialAnlys]

Artificial Analysis has been given access by Reflection and is independently benchmarking Beam

Early indicators suggest Beam will be one of the most token-efficient open models we've seen for its level of intelligence.

Congratulations @reflection_ai on the announcement!

[Brian Zhan @brianzhan1]

Flash models are where the volume is. DeepSeek V4.1 Flash and GLM 5.3 Flash lead OpenRouter’s 30-day token rankings. The models people actually deploy deserve more attention.

This is why Reflection’s Beam matters. It has 501B total parameters but activates only 23B per token. Reflection reports GLM-5.2-class reasoning on selected benchmarks at roughly one-quarter to one-third the estimated generation compute.

Open weights give developers another way to improve those economics. Beam’s Apache 2.0 release is planned for later this month, allowing developers to customize the model and optimize how they serve it.

Reflection is bringing frontier intelligence to an economically useful parameter size, then letting everyone build on it, unlocking an enormous amount of latent demand.

[Becky Sosnov @BeckySalman]

Introducing Beam: frontier reasoning at 4x the efficiency of today’s leading Western open models.

Beam is the first in a series, and we’re just getting started. We want to put more control in users hands - with sovereignty, continuity, and lower costs - and help grow a strong U.S. open ecosystem where frontier labs build on each other’s work and push one another forward.

What a team. What a moment. So proud of the people behind this and so grateful to be part of it.

[Sanyam Bhutani @bhutanisanyam1]

It’s a huge honour to be part of the team building the open weights frontier. 🙏

Super excited for everyone to try this!