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cs.SE2026
ZeroCoder: Can LLMs Improve Code Generation Without Ground-Truth Supervision?
Lishui Fan, Mouxiang Chen, Tingwei Zhu +4
Code generation is important in software engineering, and Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful paradigm to improve it through execution-based feedbac…
cs.SE2026
Balancing Latency and Accuracy of Code Completion via Local-Cloud Model Cascading
Hanzhen Lu, Lishui Fan, Jiachi Chen +3
Line-level code completion requires a critical balance between high accuracy and low latency. Existing methods suffer from a trade-off: large language models (LLMs) provide high-qu…