“The article's perspective on AI empowerment actions offers interesting insights into user experience and potential improvements.”

“The article discusses fine-tuning LLMs and the use of methods like LoRA.”

“The author realized the problem wasn't with the AI, but with the assumption that writing rules would solve the problem.”

“Since the article only references a Reddit post, a relevant quote cannot be determined.”

“Further analysis needed, but the title suggests focus on LLM fine-tuning on DGX Spark.”

“What if instead of manually firefighting every drift and miss, your agents could adapt themselves? Not replace engineers, but handle the continuous tuning that burns time without adding value.”

“In modern LLM development, Pre-training, SFT, and RLHF are the "three sacred treasures."”

“OmadaSpark, an AI agent trained with robust clinical input that delivers real-time motivational interviewing and nutrition education.”

“突然、LoRAをうまいこと使いながら、ゴ〇ジャス☆さんのような返答をしてくる化け物（いい意味で）を作ろうと思いました。”

“この記事では、VeRLというフレームワークを使ってMegatron-LMをベースにLLMをRL（PPO、GRPO、DAPO）する方法について解説します。”

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“SFT: Phase for teaching 'etiquette (format/inference rules)'; RL: Phase for teaching 'preferences (good/bad/safety)'”

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“This two-part series explores Flo Health's journey with generative AI for medical content verification.”

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“Once you train your decoder-only transformer model, you have a text generator.”

“We introduce CogCanvas, a training-free framework that extracts verbatim-grounded cognitive artifacts (decisions, facts, reminders) from conversation turns and organizes them into a temporal-aware graph for compression-resistant retrieval.”

“Transfer learning with fine-tuning substantially outperforms CNNs trained from scratch for pediatric pneumonia detection, showing near-perfect accuracy.”

“前回こちらでロジスティック回帰（およびソフトマックス回帰）でMNISTの0から9までの手書き数字の画像データセットを分類する記事を書きました。”

“"最近よく聞くMCPって何？」「RAGとファインチューニングはどう違うの？"”

“"it's not about this, it's about that, "we faced this, we faced that and we faced this" and i hate when he makes comfort stuff that makes me sick."”

“It's spectacular (in a bad way) how Gemini 3 Pro ignores the instructions.”

“"I was tired of the RAG implementation with ChatGPT, so I completely switched to Gemini Pro's 'brute-force long context'."”

“Vulcan synthesizes instance-optimal heuristics -- specialized for the exact workloads and hardware where they will be deployed -- using code-generating large language models (LLMs).”

“AdaGReS introduces a closed-form, instance-adaptive calibration of the relevance-redundancy trade-off parameter to eliminate manual tuning and adapt to candidate-pool statistics and budget limits.”

“The paper proposes using the gradient cosine similarity of low-confidence examples to predict data efficiency based on a small number of labeled samples.”

“CPJ significantly improves performance: using GPT-5-mini captions, GPT-5-Nano achieves +22.7 pp in disease classification and +19.5 points in QA score over no-caption baselines.”

“By precisely tuning the intensity ratio between fundamental and doughnut modes, we arrive at the generation of propagation-invariant vector flat-top beams for more than 5 m.”

“Our algorithm enables off-the-shelf cross-modal systems trained on 2D visual inputs to adapt online to object occlusions and differentiate features.”

“AstroReview correctly identifies genuinely accepted proposals with an accuracy of 87% in the meta-review stage, and the acceptance rate of revised drafts increases by 66% after two iterations with the Proposal Authoring Agent.”

“ConfigTuner demonstrates up to a 1.36x increase in throughput compared to Megatron-LM.”

“Xiu Yuliang shared the latest three works of the Yuanxi Lab, namely UP2You, ETCH, and Human3R.”

“EVOL-SAM3 not only substantially outperforms static baselines but also significantly surpasses fully supervised state-of-the-art methods on the challenging ReasonSeg benchmark in a zero-shot setting.”

“The paper introduces a continuation-based learning framework that combines simplified model pretraining and model homotopy transfer to efficiently generate and refine complex dynamic behaviors.”

“The molecular design of panchromatic photovoltaic materials should move beyond molecular-level optimization toward synergistic tuning among molecules, semiconductors, and electrolytes or active-layer materials, thereby providing concrete conceptual guidance for achieving efficiency optimization rather than simple spectral maximization.”

“The method achieves improved performance over state-of-the-art reconstruction methods, without task-specific supervised training or fine-tuning.”

“AutoFed consistently achieves superior performance across diverse scenarios.”

“CLoRA strikes a better balance between learning performance and parameter efficiency, while requiring the fewest GFLOPs for point cloud analysis, compared with the state-of-the-art methods.”

“DARFT suppresses strong distractors and sharpens decision boundaries without additional supervision.”

“The two-stage approach decomposes spatial reasoning into atomic building blocks and their composition.”

“The paper reports "exceptionally high deposition rate of 8.7 μm/h with less than 1% AOGs and controllable stress tuning" and "exceptional wafer-average piezoelectric coefficients (d33,f =15.62 pm/V and e31,f = -2.9 C/m2)".”

“The multilingual fine-tuned mT5 baseline outperforms most other approaches including zero-shot LLM performance for most metrics.”

“RSA explicitly incorporates risk awareness into the policy optimization process by leveraging a class of nested risk measures.”

“The proposed method significantly outperforms state-of-the-art (SOTA) methods. Compared to SOTA methods, it improves the position accuracy by 24.7% to 42.9%, and the angular accuracy by 40.9% to 78.4%.”

“The paper identifies a fundamental trade-off among storage capacity, storage time, and driving time, setting a universal limit for reliable storage.”

“RANGER achieves competitive performance in terms of navigation success rate and exploration efficiency, while showing superior ICL adaptability.”

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“RL-tuned models maintain more stable behavioral profiles and resist collapse in reasoning skills, whereas SFT models exhibit sharper drift and overfit to surface patterns.”

“HyperGRL delivers superior representation quality and generalization across diverse graph structures, achieving average improvements of 1.49%, 0.86%, and 0.74% over the strongest existing methods, respectively.”

“The approach yields significant improvements in both accuracy and efficiency and, crucially, demonstrates strong cross-domain generalization while preserving the interpretability of chain-of-thought reasoning.”