Blake Olson

M.S. Student, UT Austin

blakeolson@utexas.edu

Bio

Hello! I am a graduate student at UT Austin pursuing research in language models. Specifically, I am interested in reinforcement learning and post-training to improve reasoning ability.

My most recent work investigates the impact of curriculum learning on reasoning. We introduce E2H Reasoner (Easy2Hard), a method that improves post-training via cosine or gaussian scheduling. We compare supervised fine-tuning to reinforcement learning and measure their impact on trivial, easy, medium, and hard tasks.

Publications

2026

Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning

Shubham Parashar*, Shurui Gui*, Xiner Li*, Hongyi Ling, Sushil Vemuri, Blake Olson, Eric Li, Yu Zhang, James Caverlee, Dileep Kalathil, Shuiwang Ji

International Conference on Learning Representations (ICLR), 2026

Paper

Inference-Time Computations for LLM Reasoning and Planning: A Benchmark and Insights

Shubham Parashar*, Blake Olson*, Sambhav Khurana*, Eric Li*, Hongyi Ling, James Caverlee, Shuiwang Ji

Transactions on Machine Learning Research (TMLR), 2026

Paper Code
2025

Complex LLM Planning via Automated Heuristics Discovery

Hongyi Ling*, Shubham Parashar*, Sambhav Khurana*, Blake Olson, Anwesha Basu, Gaurangi Sinha, Zhengzhong Tu, James Caverlee, Shuiwang Ji

arXiv preprint, 2025

Paper

Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models

Cong Fu*, Xiner Li*, Blake Olson, Heng Ji, Shuiwang Ji

International Conference on Learning Representations (ICLR), 2025

Paper Code

(* indicates equal contribution)

Vitæ

Full Resume in PDF.

Blake Olson
blakeolson@utexas.edu