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cs.CL2026
Sequent-Prover: Training Agents for Formal, Checkable SMT-based Reasoning
Debargha Ganguly, Weicong Chen, Mohsen Hariri +13
We introduce Sequent-Prover, a family of verifier-using language-model agents, and the post-training framework that produces them. Agents reason in an executable SMT-LIB scratchpad…
cs.CL2026
EvolveCoder: Evolving Test Cases via Adversarial Verification for Code Reinforcement Learning
Chi Ruan, Dongfu Jiang, Huaye Zeng +2
Reinforcement learning with verifiable rewards (RLVR) is a promising approach for improving code generation in large language models, but its effectiveness is limited by weak and s…
cs.CL2025
Critique-Coder: Enhancing Coder Models by Critique Reinforcement Learning
Chi Ruan, Dongfu Jiang, Yubo Wang +1
Reinforcement Learning (RL) has emerged as a popular training paradigm, particularly when paired with reasoning models. While effective, it primarily focuses on generating response…