
Phd @CSatUSC | Ex @Alibaba_Qwen | LLMs, Agents, RL
Los Angeles
Joined December 2024
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😋 Want strong LLM reasoning without breaking the bank? We explored just how cost-effectively RL can enhance reasoning using LoRA!
[1/9] Introducing Tina: A family of tiny reasoning models with strong performance at low cost, providing an accessible testbed for RL reasoning. 🧵
Shangshang Wang retweeted
Qwen's biggest open-weight drop yet. 🚀
Meet Qwen3.8-Max: 2.4T params, 95B active, built to take a goal and come back with finished work. modelscope.ai/models/Qwen/Qw…
⚡ 1M context. Adjustable reasoning. Parallel tool calls.
💻 Coded unattended for 10+ days, building a self-evolving harness from scratch
🔬 Reproduced a research paper, then ran a 125-hour autonomous loop and beat it
🔧 Took a chip design from RTL to layout, cutting die area by 81%
Shangshang Wang retweeted
Vision is a loop, not a glance.
We introduce ActiveVision, a benchmark testing whether models can repeatedly observe, reason, and seek new visual evidence.
Humans solve 96.1%. The best frontier model: 10.6%. Fable 5—strong at reasoning and coding—scores just 3.5%.
Humans solve these visual puzzles at 96.1%. The best frontier model: 10.6%. Claude Fable 5, top of most reasoning leaderboards: 3.5%.
Introducing ActiveVision, a benchmark testing whether MLLMs can actively observe: look, hypothesize, and look again. 🧵
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Shangshang Wang retweeted
Together with my co-founders Michael @MichaelPoli6, Stefano @Massastrello and Armin @athmsx, I am excited to announce @RadicalNumerics is emerging from stealth with a $50M seed round to build general biological intelligence.
We’re also sharing an early preview of our new model Omnii, the most powerful genome language model to date.
Omnii preview link:
radicalnumerics.ai/blog/radi…
At Radical Numerics, our mission is to master the code of life, and to drive the frontier of biological AI for both design and defense.
This is our dual mandate, which comes from something our own team helped make possible.
Our founding team trained Evo and Evo 2, the largest biological AI models (40B params) trained on DNA sequences. Trillions of tokens across all of life, from microbes to mammals. It’s fully open source, and created the field now known as generative genomics.
Last year, scientists used Evo to generate the world’s first complete genome from scratch using AI. Turns out it was a bacteriophage—a type of virus. It functioned in the real world, and in this case it was harmless. But for us, it was a clear turning point.
It showed that AI is no longer just analyzing biology. It is on the cusp of generating functional lifeforms. Eventually, AI will have the power to design and control life itself.
That should make all of us incredibly excited, and incredibly uneasy. (Anyone can design DNA with a new function, and have it synthesized and delivered, like something from Amazon Prime).
The same technology that will help us cure cancer is the very technology that might create the next global pandemic, or worse, allow the creation of bioweapons that can wipe out populations.
We believe these forces are inseparable. If you work on the frontier of biology, you have to build technology to safeguard it from its misuse. Existing biosecurity tools are sorely losing the arms race, relying on outdated “have I seen this exact thing before?” style algorithms.
We founded Radical Numerics to turn the tide.
And we can’t do that by training on textbooks and natural language. We must understand the language of biology from the raw physical data itself, to reason across every molecule and modality, from DNA to proteins.
The next frontier for AI goes far beyond chatbots or video generators to models that can understand and engineer life.
Today, we’re previewing Omnii, which is already far surpassing Evo 2, and will continue improving as we scale and add new modalities (training now).
1. For human health, Omnii can read and write whole genomes (more on writing later). It’s state of the art (SOTA) on detecting causal variants for disease, and can rank Alzheimer's mutations zero-shot. We’re partnering with a diagnostics company to use Omnii for early cancer detection (pancreatic and multi-cancer).
2. For defense, Omnii is SOTA at detecting AI-generated pathogens. We benchmarked existing detection tools, and they simply can’t detect the AI-generated ones (“deepfake viruses”). We’re partnering with a US national lab to pilot Omnii for detecting the next pandemic, both natural and AI-generated.
We have a data center full of Blackwells in construction now to build the most powerful biological AI models ever. This mission takes a new kind of AI lab that can actually scale on physical, biological data: new alignment research (mid/post training), scaling long context, building out mech interp teams to dissect what these models learn, new architectures and systems designs, all from the ground up.
Our team is made up of AI researchers and scientists from top labs and institutions (e.g. Stanford, MIT, Google DeepMind), but more importantly, we all share the belief that this is the most important challenge of our lifetime. If you feel similarly, we are hiring. We aim to bring the brightest minds in AI and science together to save lives.
Thanks to our partners on this journey, led by Emergence Capital @emergencecap, with Obvious Ventures @obviousvc, Triatomic @TriatomicCap
, and Patrick Collison @patrickc. Our advisors include Eric Horvitz @erichorvitz, CSO of Microsoft, Chris Re @HazyResearch of Stanford, George Church @geochurch of Harvard, and Andrew Weber @AndyWeberNCB, former Assistant Secretary of Defense for Nuclear, Chemical and Biological Defense Programs.
Fortune article: fortune.com/2026/06/15/exclu…
Jobs: radicalnumerics.ai/join-us
Many thanks to @thinkymachines for the Tinker grant!
Our plan: we know that LoRA matches full-param RL by exploiting a tiny subspace—now we’re testing whether exploration can push it further. Stay tuned.
We now know that LoRA can match full-parameter RL training (from nitter.gq/thinkymachines/status/… and our Tina paper arxiv.org/abs/2504.15777), but what about DoRA, QLoRA, and more?
We are releasing a clean LoRA-for-RL repo to explore them all.
github.com/shangshang-wang/T…
LoRA makes fine-tuning more accessible, but it's unclear how it compares to full fine-tuning. We find that the performance often matches closely---more often than you might expect. In our latest Connectionism post, we share our experimental results and recommendations for LoRA.
thinkingmachines.ai/blog/lor…
(Q)DoRA-with-Cache-based GRPO
The standard DoRA layer recalculates the weight norm and magnitude scale on every forward pass. DoRA with Cache optimizes this by caching these expensive computations.
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Our code is built on torchtune @PyTorch. We hope that our implementation can also contribute to their new repo for post-training! github.com/meta-pytorch/torc…
github.com/shangshang-wang/T…
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LoRA is real for Reasoning.
nitter.gq/thinkymachines/status/…
LoRA makes fine-tuning more accessible, but it's unclear how it compares to full fine-tuning. We find that the performance often matches closely---more often than you might expect. In our latest Connectionism post, we share our experimental results and recommendations for LoRA.
thinkingmachines.ai/blog/lor…
Sparse autoencoders (SAEs) can be used to elicit strong reasoning abilities with remarkable efficiency.
Using only 1 hour of training at $2 cost without any reasoning traces, we find a way to train 1.5B models via SAEs to score 43.33% Pass@1 on AIME24 and 90% Pass@1 on AMC23.
Curious about the details for these efficiency claims?
We open-source everything for full reproducibility:
Paper: arxiv.org/abs/2506.09967
Blog: shangshangwang.notion.site/r…
Code: github.com/shangshang-wang/R…
Model: huggingface.co/Resa-Yi
Training Logs: wandb.ai/upup-ashton-wang-us…
This is another amazing collaboration with Julian @julian_asilis , Omer @oemerakgull , Enes, Oliver @olliezliu and Deqing @DeqingFu in the course taught by Willie @willieneis (both the teacher and the advisor). Thanks everyone!
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We also want to express our gratitude to the broader open-source community. This research was made possible by leveraging numerous publicly available resources from DeepScaleR @Agentica_ , STILL, OpenThoughts @bespokelabsai , OpenR1 @huggingface , LIMR, and OpenRS projects.
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👧 Check out more about Tina down below!
Paper: arxiv.org/abs/2504.15777
Notion Blog: shangshangwang.notion.site/t…
Code: github.com/shangshang-wang/T…
Model: huggingface.co/Tina-Yi
Training Logs: wandb.ai/upup-ashton-wang-us…
Tina's avatar is generated by GPT-4o based on KYNE's girls.
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