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cs.LG2026
Trading Human Curation for Synthetic Augmentation in RLVR
Akshansh, Leonardo Rosa Rodrigues, Michael Korostelev +2
The supply of high-quality training tasks is a central bottleneck for reinforcement learning from verifiable rewards (RLVR) on agentic language models. Each task requires a sandbox…
cs.LG2026
Biological Context Does Not Reliably Improve LLM-Guided Experimental Optimization
Marilyn Zhang, Tianfeng Chen, Fabián Barzuna +2
Autonomous labs increasingly deploy large language models (LLMs) as closedloop biology optimizers that propose a design, read the outcome, then propose again. This is partly becaus…