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cs.CL2026
PRISM: A Unified Framework for Post-Training LLMs Without Verifiable Rewards
Mukesh Ghimire, Aosong Feng, Liwen You +3
Current techniques for post-training Large Language Models (LLMs) rely either on costly human supervision or on external verifiers to boost performance on tasks such as mathematica…
cs.CL2025
PCMind-2.1-Kaiyuan-2B Technical Report
Kairong Luo, Zhenbo Sun, Xinyu Shi +9
The rapid advancement of Large Language Models (LLMs) has resulted in a significant knowledge gap between the open-source community and industry, primarily because the latter relie…
cs.CL2024★ 1 cited
AIDE: Attribute-Guided MultI-Hop Data Expansion for Data Scarcity in Task-Specific Fine-tuning
Jiayu Li, Xuan Zhu, Fang Liu +1
Fine-tuning large language models (LLMs) for specific tasks requires diverse, high-quality training data. However, obtaining sufficient relevant data remains a significant challeng…