Sungmin Cha @_sungmin_cha
Faculty Fellow @nyuniversity | PhD @SeoulNatlUni sites.google.com/view/sungmin-c… Manhattan, NY Joined July 2019-
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Sungmin Cha retweeted
🌀New work: Era of Real-World Human Interaction 🌀 📝: arxiv.org/abs/2509.25137 - RL *directly* from User Conversations - Organic replies + long-term history are learning signal - Trained on WildChat, beats RLHF at *user* level -> the future for personal Super Intelligence? 🧵1/6
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Sungmin Cha retweeted
Finally had a chance to listen through this pod with Sutton, which was interesting and amusing. As background, Sutton's "The Bitter Lesson" has become a bit of biblical text in frontier LLM circles. Researchers routinely talk about and ask whether this or that approach or idea…
Finally had a chance to listen through this pod with Sutton, which was interesting and amusing. As background, Sutton's "The Bitter Lesson" has become a bit of biblical text in frontier LLM circles. Researchers routinely talk about and ask whether this or that approach or idea…
Sungmin Cha retweeted
It was great to have @kchonyc Kyunghyun Cho visit us in Columbia University and talk about metrics and evaluation. We should rethink our benchmark and the eval metrics, don't just follow other popular papers!
We offered a minimal working explanation on a similar phenomenon: low-recall reference models structurally limit alignment, no matter how strong the preference signal! Check details here: x.com/_sungmin_cha/s…
We offered a minimal working explanation on a similar phenomenon: low-recall reference models structurally limit alignment, no matter how strong the preference signal! Check details here: x.com/_sungmin_cha/s…
This resonates with our findings! Once a model’s recall is limited, alignment signals can’t fully align it. Larger, better-pretrained models preserve recall — making downstream RL much more effective! Check more details here: x.com/_sungmin_cha/s…
This resonates with our findings! Once a model’s recall is limited, alignment signals can’t fully align it. Larger, better-pretrained models preserve recall — making downstream RL much more effective! Check more details here: x.com/_sungmin_cha/s…
Sungmin Cha retweeted
XYZ Last Exam becoming a recurring benchmark name was not on my bingo card man. Progress is going to go right past these in a few years and we'll be onto the next evals before we know it.
XYZ Last Exam becoming a recurring benchmark name was not on my bingo card man. Progress is going to go right past these in a few years and we'll be onto the next evals before we know it.
Sungmin Cha retweeted
perhaps this is what so-called frontier labs do: RL before KD. 🧐 stay tuned for a detailed 🧵 from @_sungmin_cha ! the preprint link ⬇️
Sungmin Cha retweeted
We show why the common #KD → #Align workflow for LLMs fails: low-recall references create a trap that blocks rare but important behaviors. ✅ Solution: #Align → #KD. Align first, then distill. Better rewards, precision, and stability!😀
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