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cs.CL2025
What Makes Large Language Models Reason in (Multi-Turn) Code Generation?
Kunhao Zheng, Juliette Decugis, Jonas Gehring +3
Prompting techniques such as chain-of-thought have established themselves as a popular vehicle for improving the outputs of large language models (LLMs). For code generation, howev…
cs.CL2025
The KoLMogorov Test: Compression by Code Generation
Ori Yoran, Kunhao Zheng, Fabian Gloeckle +3
Compression is at the heart of intelligence. A theoretically optimal way to compress any sequence of data is to find the shortest program that outputs that sequence and then halts.…
cs.CL2025
RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning
Jonas Gehring, Kunhao Zheng, Jade Copet +4
Large language models (LLMs) deployed as agents solve user-specified tasks over multiple steps while keeping the required manual engagement to a minimum. Crucially, such LLMs need…