Nikhil Barhate @nikhilbarhate99
ML @scale_AI | prev @AMD @mila_quebec nikhilbarhate99.github.io San Francisco, CA Joined June 2015-
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🚀 Introducing SWE-Bench Pro — a new benchmark to evaluate LLM coding agents on real, enterprise-grade software engineering tasks. This is the next step beyond SWE-Bench: harder, contamination-resistant, and closer to real-world repos.
Very excited to share our paper "De novo Design of All-atom Biomolecular Interactions with RFdiffusion3", now on BioRXiv. biorxiv.org/content/10.110… 1/n
create... explore... repeat
Thanks AK for sharing our work! Unlike autoregressive LLMs, diffusion LLMs can be conditioned on future reasoning hints during generation through inpainting 🧩, enabling guided exploration toward correct solutions. We show that applying inpainting-guided exploration in RL…
Thanks AK for sharing our work! Unlike autoregressive LLMs, diffusion LLMs can be conditioned on future reasoning hints during generation through inpainting 🧩, enabling guided exploration toward correct solutions. We show that applying inpainting-guided exploration in RL… https://t.co/M97q7IriEU
Moonlighting a bit: we implement Online RL for VLA models with @Verl🤖, and find simple outcome rewards can work surprisingly well！ The code is open-sourced: github.com/PRIME-RL/Simpl…. 🚀Thrilled to introduce SimpleVLA-RL! With only one trajectory per task for SFT, SimpleVLA-RL…
🚨🚨New paper on core RL: a way to train value-functions via flow-matching for scaling compute! No text/images, but a flow directly on a scalar Q-value. This unlocks benefits of iterative compute, test-time scaling for value prediction & SOTA results on whatever we tried. 🧵⬇️
We have been doing work on scaling laws for off-policy RL for some time now and we just put a new paper out: arxiv.org/abs/2508.14881 Here, @preston_fu @_oleh lead a study on how to best allocate compute for training value functions in deep RL: 🧵⬇️
Instructions/reasoning are now everywhere in retrieval - we want embeddings to do it all! 🚀 But... is it even possible? 🤔 Turns out, it's not possible for single-vector models 😱 theoretically and empirically! To make it obvious we OSS a simple eval SoTA models flop on! 🧵
@suchenzang @yaroslavvb No way!! I literally wrote libheatmap precisely to make heatmaps of event locations in dota2, in 2013 lucasb.eyer.be/articles/color…
very recently (as of v0.1.3) figured out what i think is the "right" way to handle Rubric-level state + objects being made available to reward functions inside verifiers previously, you'd just declare extra things globally (def an anti-pattern, always bugged me) and i'd manually…
VLAs offer an avenue for generalist robot policies; however, naively following the action predictions leads to brittle or unsafe behaviours. We introduce VLAPS, which integrates model-based search with pre-trained VLA policies to improve performance without additional training.
Cooking up cool stuff at work 🍜🤖 had a great time building model debate for data quality!
Cooking up cool stuff at work 🍜🤖 had a great time building model debate for data quality!
I've written the full story of Attention Sinks — a technical deep-dive into how the mechanism was developed and how our research ended up being used in OpenAI's new OSS models. For those interested in the details: hanlab.mit.edu/blog/streaming…
I spent the past months investigating: Can we trust reasoning models' CoTs? Researchers showed that LLMs aren't always faithful, but that's not the full story. LLMs are very faithful when the reasoning is complex, and unfaithful CoTs remain monitorable! Check out my latest work🥳
I spent the past months investigating: Can we trust reasoning models' CoTs? Researchers showed that LLMs aren't always faithful, but that's not the full story. LLMs are very faithful when the reasoning is complex, and unfaithful CoTs remain monitorable! Check out my latest work🥳
Failing on 𝐥𝐚𝐫𝐠𝐞-𝐬𝐜𝐚𝐥𝐞 𝐑𝐋 with VeRL? ⚠️ Mixing inference backend (𝐯𝐋𝐋𝐌/𝐒𝐆𝐋𝐚𝐧𝐠) with training backends (𝐅𝐒𝐃𝐏/𝐌𝐞𝐠𝐚𝐭𝐫𝐨𝐧) 𝐬𝐞𝐜𝐫𝐞𝐭𝐥𝐲 𝐭𝐮𝐫𝐧𝐬 𝐲𝐨𝐮𝐫 𝐑𝐋 𝐢𝐧𝐭𝐨 𝐨𝐟𝐟-𝐩𝐨𝐥𝐢𝐜𝐲 — even if they share the same weights! 📉 Blog:…
Genie 3 is here - it can generate an entire world simulation that you can interact with in real-time, just from a text prompt! It's pretty mind-blowing really when you stop to think about it, and it's rapidly improving - one day we will be able to build the Holodeck for real!
🚀 Excited to share our #CoRL2025 paper! See you in Korea 🇰🇷!🎉 We present ParticleFormer, a Transformer-based 3D world model that learns from point cloud perception and captures complex dynamics across multiple objects and material types ! 🌐 Project website:…
July has been a big month for Viser! - Released v1.0.0😊 - We did some writing Some demos👇
Excited to have our AI research published in @Nature today. Proud of the @ProfluentBio team and the extensive final version available under open-access. OpenCRISPR is a milestone. It's the first successful demonstration of editing the human genome with a molecule fully designed…
Excited to have our AI research published in @Nature today. Proud of the @ProfluentBio team and the extensive final version available under open-access. OpenCRISPR is a milestone. It's the first successful demonstration of editing the human genome with a molecule fully designed…
Excited to share Flow Matching Policy Gradients: expressive RL policies trained from rewards using flow matching. It’s an easy, drop-in replacement for Gaussian PPO on control tasks.
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