A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Juncheng Wu, Sheng Liu, Haoqin Tu, et al.
This paper introduces a fine-grained evaluation framework decomposing LLM reasoning into knowledge correctness and reasoning quality, revealing that SFT improves accuracy but degrades reasoning, while RL enhances medical reasoning by pruning inaccura
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LearnLM enhances Gemini for pedagogical instruction by co-training SFT and RLHF, outperforming leading LLMs in tutoring scenarios.
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A pipeline that generates system messages and aligned assistant responses for SFT datasets lacking them, using LLM-as-a-judge verification.
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RL improves generalization in text and visual tasks while SFT primarily memorizes training data, with inference-time compute scaling further enhancing RL gains.
Zihan Liu, Zhuoling Yang, Yang Chen, et al.
AceReason-Nemotron 1.1 scales SFT data and uses stage-wise RL with tuned temperatures to achieve state-of-the-art reasoning in 7B models.
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A 14B parameter reasoning language model derived from Deepseek-R1-Distilled-Qwen-14B, trained with SFT and model merging, fully open-sourced.
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A large language model built on Mistral Small, enhanced with SFT, RLVR, and inference optimization for Indian languages and reasoning tasks.
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Phi-4-Reasoning and its RL-enhanced variant achieve strong reasoning performance in a 14B model via curated SFT data and outcome-based RL.
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Tulu V3 presents a fully open post-training recipe for Llama 3.1 models, achieving state-of-the-art performance via SFT, DPO, and RLVR.