This work presents LoopCoder pre-trained on 12T+ code and general tokens, along with LoopCoder-Thinking and LoopCoder-Instruct variants, the first large-scale looped transformer for code, achieving comparable performance to standard dense architectures with more parameters.
Looped Mamba and Looped Hybrid Mamba-Transformer architectures, which repeatedly apply a shared Mamba block to introduce explicit finite-depth recurrent computation, are investigated and adaptive exit-state selection improves downstream performance at intermediate depths, while actual inference-time savings require add...
Zhenxuan Yu, Takeshi Kojima, Yutaka Matsuo et al.· 0 citations
Two novel contributions are introduced: CodeEval and CodeQual, an open-source execution framework that provides researchers with a ready-to-use evaluation pipeline for evaluating and improving LLMs in software engineering contexts, encompassing both functional correctness assessment and subjective code quality evaluati...
This work evaluates native and retrofitted looped language models on API-Bank, BFCL, and NESTful, comparing looped and non-looped models trained under matched supervised fine-tuning recipes and varying recurrent depth at inference time to suggest that looped language models are a promising architecture for agentic syst...
A. C. Popescu, Haitz Sáez de Ocáriz Borde, Pietro Liò· 2 citations
Deep learning (DL) compilers such as Apache TVM translate high-level models into optimized low-level code through multi-stage compilation pipelines. While recent testing efforts have improved fuzzing of optimization stages, they still face two key challenges: (i) the lack of semantics-preserving test models, leading to...
Yifei He, Fangyu Yang, Ting Su et al.· Annual International Compute...· 0 citations
An MLIR (Multi-Level Intermediate Representation) based compilation method for large language models, illustrated using two dialects of operators, TopOp and TpuOp, supporting a variety of generative models including the Qwen, Llama, InternVL, and MiniCPM-V series.
Pengchao Hu, Zhibin Xin, Yifan Chen et al.· 0 citations
This work proposes a novel Reinforcement Learning from AI Feedback (RLAIF) framework, finding that specifications can be iteratively refined by leveraging feedback from the LLM's own outputs, creating a self-improving loop.
Jaykithan Y Patel· SIGSOFT FSE Companion· 0 citations
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