6 citations · 7 across the 12 of their papers we have counts for
3 papers · 1 filter
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…
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…
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay
Natasha Butt, Blazej Manczak, Auke Wiggers +4
Large language models are increasingly solving tasks that are commonly believed to require human-level reasoning ability. However, these models still perform very poorly on benchma…