Chenghao Yang

@chrome1996

Senior AS @Microsoft. Ph.D. @UChicago Ex-SR @google Ex-Scientist @AWS. Ex-RA @jhuCLSP @columbianlp @TsinghuaNLP. Ex-Intern @IBM @AWS. Opinions are my own.

Redmond, WA

yangalan123.github.io

Born December 16, 1996

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Chenghao Yang
@chrome1996
Jun 24, 2025
Have you noticed… 🔍 Aligned LLM generations feel less diverse? 🎯 Base models are decoding-sensitive? 🤔 Generations get more predictable as they progress? 🌲 Tree search fails mid-generation (esp. for reasoning)? We trace these mysteries to LLM probability concentration, and
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Chenghao Yang
@chrome1996
Sep 15
Honored to have the opportunity to learn OPD and RL with a wonderful team: Boyan Li, @BaleCcchen, @xiye_nlp, and @henryzhao4321! It makes a lot of sense to run OPD to warm up for RL training instead of naive SFT, and this paper uses clear, solid, and thorough experiments to
Xi Ye
@xiye_nlp
Sep 15
New 📜 on the interplay between OPD and RLVR 💡Don’t skip RL after OPD We find that after using OPD for reasoning, adding an RL phase consistently improves performance. Across reasoning tasks, OPD→RL outperforms pure OPD, pure RLVR, and many joint OPD+RL methods.
Chenghao Yang
@chrome1996
Sep 14
Excited to join this open-source effort in my spare time! I’ve long believed that LLM alignment should be grounded in the diversity of real human behavior—not a handful of stereotypes. Social/human/user simulation is taking off (@humansand, @simile_ai, ...), but how do we know
Social Simulation Arena
@socialsimarena
Sep 14
Social simulation is now a product. Evaluation still has no common standard. What if we tested every simulator against the real future? We built Social Simulation Arena with researchers from MIT, Stanford, Harvard, CMU, UC Berkeley, and beyond. Persona-prompted populations.
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Chenghao Yang
@chrome1996
Aug 17
🎉 Our paper “LLM Probability Concentration: How Alignment Shrinks the Generative Horizon” is accepted by TMLR! The short version: 📉 LLM generation usually self-narrows ✂️ Alignment compresses the horizon further ⚡ Unexpected context can reopen it locally We study these
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Chenghao Yang
@chrome1996
Mar 21
00:19
BranchingFactor v1.1 just dropped! 🚀 (Yes — it’s an actively updated paper.) (arxiv.org/abs/2506.17871) As models rely more on post-training, understanding the synergy between pre-training and alignment becomes crucial. Branching Factor (BF) offers a simple way to track the
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Chenghao Yang
@chrome1996
Jun 15
Excited to contribute to this work! 👁️📝 As someone relatively new to the multimodal world, I've always wanted to explore how we can better ground LLMs. But before diving deep into modeling, I believe we need a better understanding of what our benchmarks are actually evaluating.
Harvey Yiyun Fu
@harveyiyun
Jun 15
Seeing👀 is not reasoning🤔? Introducing our new blog post. We tested 8 open VLMs on 9 VQA benchmarks, and found that a lot of "visual" accuracy isn't visual at all. Many benchmarks can be solved without ever looking at the image, and some VLMs reason better from text than from