Nan Jiang @nanjiang_cs
machine learning researcher, with focus on reinforcement learning. assoc prof @ uiuc cs. Course on RL theory (w/ videos): https://t.co/vqVKwY4RJE nanjiang.cs.illinois.edu Joined November 2017-
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Having used LLMs for helping with research (usually obscure issues/questions in technical lemmas), I'm getting the feeling that free ver of chatgpt is better than gemini 2.5 pro on this...?
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For course notes that I update over years, what's the best practice? keep only the latest file, or keep all the major historical versions?
I was surprised by how many didnt know that (1) per token MLE is whole seq MLE, and (2) PG at token level same as PG at seq level (optimizkng one big combinatorial action). story is different if you introduce fitted critic/Q-values or intermediate resets.
I was surprised by how many didnt know that (1) per token MLE is whole seq MLE, and (2) PG at token level same as PG at seq level (optimizkng one big combinatorial action). story is different if you introduce fitted critic/Q-values or intermediate resets.
Nan Jiang retweeted
Microsoft Research New York City is seeking applicants for multiple Postdoctoral Researcher positions in ML/AI! These are positions for up to 2 years, starting in July 2026. Application deadline: October 22, 2025
shower thought: we model reward as fn of prompt & response but there are legit exceptions when questions are self-referential eg “r u strong in reasoning?” whose correct answer is policy dependent
useful & practical guidance for alignment! theorist nitpick: chi-square is more informative than KL on this matter. not that I expect huge diff in reality...
useful & practical guidance for alignment! theorist nitpick: chi-square is more informative than KL on this matter. not that I expect huge diff in reality...
from @PBSSpaceTime. once I told @JohnCLangford—who wrote a general relativity(!) paper around the time I interned at MSR—abt them and their first host being a Physics PhD working then at NSF. Turns out that was his coauthor on the GR paper… such a small world!
my 2nd blogpost: a rabbit hole I went down with a (wrong) trajectory-coupling interpretation of bisimulation metrics. gets quite technical but I'm glad I set up the blog, otherwise I wouldn't know where to share this :) nanjiang.cs.illinois.edu/2025/08/25/cou…
Nan Jiang retweeted
Once I supervised a project about pollen forecasting. That was eons ago with decision trees. After a year we did not beat the baseline that consists of using the previous day's value.
Once I supervised a project about pollen forecasting. That was eons ago with decision trees. After a year we did not beat the baseline that consists of using the previous day's value.
it is really an unfortunate convention that we choose to use d for occupancy in RL (e.g., d^pi)...
Nan Jiang retweeted
Announcing the first workshop on Foundations of Language Model Reasoning (FoRLM) at NeurIPS 2025! 📝Soliciting abstracts that advance foundational understanding of reasoning in language models, from theoretical analyses to rigorous empirical studies. 📆 Deadline: Sept 3, 2025
Nan Jiang retweeted
@DimitrisPapail @code_star chain rule for KL divergence: you have a token-level loss (good for optimization) but it gives you sequence-level control over behavior
Stats twitter: for IV regression E[Z X'] θ ≈ E[Z Y] (Z,X in R^d, θ in R^d, Y scalar), if I query a new x, LLM told me uncertainty is controlled by x' E[Z X']^{-1} E[ZZ'] (E[ZX']')^{-1} x, generalizing LR case (see concentration in pic). Is this legit? Ref to concentration bound?
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