Sagnik Mukherjee
I’m a PhD candidate at UIUC, working on Reinforcement Learning for LM post-training with Dilek Hakkani-Tür and Hao Peng. I was the first to discover emergent structures in on-policy training, which later spawned a research thread at the intersection of optimization and on-policy training in LLMs. Lately I’ve been thinking about agentic sequential decision making — specifically, the delayed consequences of actions and partial observability. Feel free to reach out if you’d like to collaborate or chat.
News
Sept 2026New preprint: Understanding LLM Parameter Update Sparsity through the Lens of Fisher — we explain, theoretically, why on-policy data causes sparse updates.
Sept 2026VASR was accepted at NeurIPS 2026.
Sept 2026Gave a talk, “Optimization Geometry in LLM Post-Training”, at the UIUC Machine Learning Seminar.
Jul 2026Presented Do We Need Adam? as an Oral at the ICML 2026 main conference in Seoul.
May 2026Started my internship at Microsoft Frontier Tuning, hosted by Harsh Jhamtani and Jacob Andreas.
Mentoring
I have had the opportunity to work with some amazing students.
- Pavan Jayasinha — now at OpenAI Do We Need Adam? Surprisingly Strong and Sparse Reinforcement Learning with SGD in LLMs
- Yufan Zhang — visiting student at UIUC Understanding LLM Parameter Update Sparsity through the Lens of Fisher
- Saket Vispute — student at IIT Bombay