Parameter Exploration for RLVR via Variational Learning
Explore in parameter space, not just token space: sampling policies from a posterior (3PO) beats GRPO on math and code at near-identical FLOPs.
tap a tile to plant or clear a bush
drag the snack to a new spot
tap the cat to pet it (at own risk)

PhD student · INSAIT & UKP Lab · Sofia
Hi! I'm a second-year PhD student at INSAIT and UKP Lab, advised by Prof. Iryna Gurevych. I work on LLM post-training: using reinforcement learning to align and improve language models, lately with verifiable rewards (RLVR). Before that, I did an integrated B.Tech + M.Tech at IIT Kharagpur, with research internships at ANU and UBC along the way.
Outside of work I'm usually at the gym, on the tennis court, or at a board-game café. I also have a major soft spot for Sofia's street cats, though the feeling is rarely mutual (exhibit A: the cat up top).
Explore in parameter space, not just token space: sampling policies from a posterior (3PO) beats GRPO on math and code at near-identical FLOPs.
A controlled, execution-grounded testbed for training code verifiers with RLVR. The best recipe depends on scale, and cheaper recipes can match the full pipeline.
Summarizing legal cases from jurisdictions with no reference summaries, using unlabeled target data and silver summaries.
A hybrid pipeline of fine-tuned code models and rules for generating structured Python parameter docstrings.