PhD student at Boston University | Prev @MSFTResearch
Boston, USA
Joined October 2023
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Anush Kini retweeted
Recently drove the entire west of this country ( from San Diego to Seattle) over 2 days! Saw all kinds of terrain- mountains, deserts, farms, buildings, water, city, village, town etc.
Very beautiful ✨
Anush Kini retweeted
There was a time when you owned the software you bought.
I play Minecraft most evenings with my son for an hour. Today, the license server was down, and the game wouldn’t start. We just sat there, staring at the launcher, watching it try to verify our license. A game I paid for. Installed locally. All files right there on my hard drive, and yet, I couldn’t open it.
It struck me how subtly things have shifted over the years. You don’t own your tools anymore. You’re renting access—sometimes to your favorite games, sometimes to your productivity apps, even to your creative work. When a server goes down or a company changes its terms, the door quietly closes on something that used to be yours.
It’s convenient, yes. Seamless updates, cloud saves, multiplayer access. But that convenience has a hidden cost: control.
My son just asked, “Why can’t we play if it’s on our computer?” I didn’t have a good answer. Maybe that’s the most telling sign of all.
Anush Kini retweeted
(1/4) Typical LLM post-training mechanisms have a hard time learning models that can produce diverse responses. To fix this we introduce 𝐃𝐐𝐎 (𝐃𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧), a method for post-training LLMs to generate diverse high-quality responses. Here is the paper link:
arxiv.org/abs/2509.04784
Anush Kini retweeted
My First Blog!
abinezer.github.io/posts/blo…
Would love feedback and comments!
Thanks to my friends @Ability_Guy and @RoshniPoddar2 for reviewing!
You can find me and @Sayakrayc presenting our work today at Poster Session 3 (Poster #913) – come say hello!
#ICML2024
Excited to share that our paper “Provably Robust DPO: Aligning Language Models with Noisy Feedback” has been accepted at #ICML2024!
We introduce Robust DPO, an unbiased estimate of the DPO loss that is robust to preference noise in the data.
arxiv.org/abs/2403.00409
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Anush Kini retweeted
28.33% 🔥🔥 on SWE-bench Lite!
Releasing MASAI: Modular Architecture for Software-engineering AI agents
Modularity helps achieve highest resolution rate (28.33%) at <$2 avg. cost/issue on SWE-bench Lite
@amuseddaman @twm_as @nalin_wadhwa @AbhavM @SaitejaUtpala @rkbairi @naga86
arxiv.org/abs/2406.11638
Excited to share that our paper “Provably Robust DPO: Aligning Language Models with Noisy Feedback” has been accepted at #ICML2024!
We introduce Robust DPO, an unbiased estimate of the DPO loss that is robust to preference noise in the data.
arxiv.org/abs/2403.00409
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We provide a general framework that can be adapted to other preference optimizations methods (e.g. SLiC, IPO) and other preference models (e.g. Probit, Placket-Luce). We provide first theoretical guarantees for practical preference optimization algorithms.
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Robust DPO is available in the TRL Library: huggingface.co/docs/trl/dpo_…
Joint work with @Sayakrayc and @naga86 at @MSFTResearch
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