Audrey Huang
@auddery
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Joined May 2024
RT @geneli0: like everyone else i am hopping on the blog post trend.
gene.ttic.edu
A personal website.
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RT @jasondeanlee: I have been waiting 6 weeks for you to come and schedule us for the blind installation. We have spent 20 hours on phone o….
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RT @nanjiang_cs: missing ICML, and I used this week to write my first technical blog on some recent thoughts on two different roles of simu….
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RT @Nived_Rajaraman: Announcing the first workshop on Foundations of Post-Training (FoPT) at COLT 2025!. 📝 Soliciting abstracts/posters exp….
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RT @canondetortugas: RL and post-training play a central role in giving language models advanced reasoning capabilities, but many algorithm….
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RT @canondetortugas: Is Best-of-N really the best we can do for language model inference? . New algo & paper: 🚨InferenceTimePessimism🚨. Le….
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RT @canondetortugas: Akshay presenting InferenceTimePessimism, a new alternative to BoN sampling for scaling test-time compute. From our re….
arxiv.org
Inference-time computation offers a powerful axis for scaling the performance of language models. However, naively increasing computation in techniques like Best-of-N sampling can lead to...
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RT @liyzhen2: #AISTATS2025 day 3 keynote by Akshay Krishnamurthy about how to do theory research on inference time compute 👍.@aistats_conf….
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RT @canondetortugas: Our work on language model self-improvement will appear as an Oral at ICLR! See you in Singapore!. .
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RT @canondetortugas: Given a high-quality verifier, language model accuracy can be improved by scaling inference-time compute (e.g., w/ rep….
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RT @canondetortugas: Check out the paper for more details: Joint work w/ Audrey Huang (@auddery), Adam Block, Dhru….
arxiv.org
Recent work in language modeling has raised the possibility of self-improvement, where a language models evaluates and refines its own generations to achieve higher performance without external...
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RT @canondetortugas: New preprint: Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning. We show that good old fas….
arxiv.org
Imitation learning (IL) aims to mimic the behavior of an expert in a sequential decision making task by learning from demonstrations, and has been widely applied to robotics, autonomous driving,...
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