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cs.AI2026
ReSyn: Autonomously Scaling Synthetic Environments for Reasoning Models
Andre He, Nathaniel Weir, Kaj Bostrom +4
Reinforcement learning with verifiable rewards (RLVR) has emerged as a promising approach for training reasoning language models (RLMs) by leveraging supervision from verifiers. Al…
cs.CL2026
VERGE: Formal Refinement and Guidance Engine for Verifiable LLM Reasoning
Vikash Singh, Darion Cassel, Nathaniel Weir +2
Despite the syntactic fluency of Large Language Models (LLMs), ensuring their logical correctness in high-stakes domains remains a fundamental challenge. We present a neurosymbolic…