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cs.CL2026
Beneath the Diff: Diagnosing and Mitigating Algorithmic Mode Collapse in Code-Level Autonomous Research Loops
Bowei He, Weixu Zhang, Yili Jin +1
Code-level autonomous research loops (ARLs) have recently emerged as a concrete object of study in automated machine learning research. In such loops, an LLM agent proposes modific…
cs.CL2025★ 3 cited
A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?
Qiyuan Zhang, Fuyuan Lyu, Zexu Sun +10
As enthusiasm for scaling computation (data and parameters) in the pretraining era gradually diminished, test-time scaling (TTS), also referred to as ``test-time computing'' has em…
cs.CL2024
Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-Translation
Tianyu Zheng, Shuyue Guo, Xingwei Qu +7
In this paper, we introduce Kun, a novel approach for creating high-quality instruction-tuning datasets for large language models (LLMs) without relying on manual annotations. Adap…