The paper identifies why high‑performing code models falter in real software engineering tasks, introduces the SEAlign alignment framework that targets key decision points in agent trajectories, and demonstrates substantial gains on SWE‑Bench, HumanEvalFix, and user‑centric evaluations.
This guide maps the end-to-end process for building a small AI model—from leveraging open-source base models and applying SFT with LoRA/QLoRA, through alignment techniques like DPO or ORPO, to low-cost distillation and final quantization for local deployment, while recommending free GPU resources and essential tooling.
This article breaks down the three core stages of large language model training—pretraining, supervised fine‑tuning, and alignment—detailing their objectives, typical data formats, scale requirements, and the latest techniques such as RLHF and DPO.
This guide walks through the complete end‑to‑end process of training a large language model from scratch, covering data collection, cleaning, tokenization, pre‑training objectives and engineering, post‑training alignment methods, scaling laws, over‑fitting mitigation, and gradient‑stability techniques.
This article explores the importance of post‑training for large language models, explains scaling laws for pre‑ and post‑training, details common fine‑tuning methods (full, PEFT, LoRA), outlines alignment techniques such as RLHF, DPO, PPO, and presents practical workflows using Llama 3 and DeepSeek‑R1, while also discussing test‑time reasoning optimizations.
This article reviews the 2025 "Advances and Challenges in Foundation Agents" paper, defining the Foundation Agent concept, detailing its seven core components, exploring self‑evolution, multi‑agent collaboration, and the safety and alignment challenges required to build trustworthy, autonomous AI systems.
Researchers at Bilibili present Index‑Anisora, an open‑source anime video generation framework that builds a 30k‑sample reward dataset, introduces the multi‑dimensional AnimeReward model and a Gap‑Aware Preference Optimization (GAPO) method, and demonstrate through extensive automatic and human evaluations that their approach significantly outperforms baseline video generators.
This article surveys the post‑training pipelines of major open‑source large language models released this year, detailing their alignment algorithms, data synthesis, reward modeling, DPO/GRPO variants, long‑context handling, tool use, and model‑averaging techniques, and highlights emerging trends such as data‑centric pipelines and iterative weak‑to‑strong alignment.
The article reports the departure of OpenAI research VP Lilian Weng, provides the full transcript of her recent AI safety and alignment presentation at a Bilibili event, and discusses broader concerns about OpenAI's safety culture, reinforcement learning from human feedback, and the importance of collective involvement in AI safety.
Vertical domain large language models, fine‑tuned on specialized data, deliver higher expertise and task performance, but require continual knowledge updates and careful alignment; techniques such as BPO‑guided instruction tuning (+1.8% accuracy), Reflexion‑based Text2API (+4% API correctness), advanced RAG preprocessing, and SFT combined with ORPO (+5.2% gain) demonstrate notable improvements while underscoring remaining challenges and collaborative opportunities.
The report presents the open‑source Index‑1.9B family—base, pure, chat, and character variants—detailing benchmark results, pre‑training optimizations such as a normalized LM‑Head and deeper‑slim architectures, the importance of modest instruction data, alignment via SFT/DPO, role‑play enhancements with RAG, and acknowledges remaining safety and factual limitations.
This article surveys the current RLHF ecosystem, comparing on‑policy methods like PPO with off‑policy approaches such as DPO, and examines recent variants—including ReMax, GRPO, DPOP, TDPO, and ORPO—highlighting their algorithmic differences, resource trade‑offs, and practical performance insights.
This article reviews the rapid progress of large language models (LLMs), covering their historical development, scaling laws, emergent abilities, core technologies such as training and alignment, resource ecosystems, evaluation methods, safety concerns, and prospective research challenges.
This article walks through the step‑by‑step implementation of alignment functions—left, center, right, top, middle, and bottom—in a graphics editor by computing AABB boxes, deriving a mixed bounding box, and applying concise JavaScript loops to adjust element positions.
The article summarizes the technical innovations behind GPT‑4, highlighting its multimodal abilities, improved alignment methods, scaling‑law‑based performance prediction, and remaining limitations, while referencing the official OpenAI technical report and community analyses.
This guide outlines practical UI design techniques—such as typographic hierarchy, strategic spacing, precise alignment, consistent styling, micro‑textures, and proper image proportions—to help designers create visually rich, high‑quality interfaces that delight users and strengthen brand perception.
This article explains how ChatGPT builds on GPT‑3, describes the supervised‑plus‑reinforcement learning (RLHF) pipeline that fine‑tunes the model, compares model capability with consistency, and discusses the performance evaluation and remaining limitations of large language models.
This article explains how the Wukong low‑code visual platform implements component drag‑and‑drop, covering material‑to‑canvas dragging, intra‑canvas movement, resizing via eight handles, alignment guide generation, performance optimizations using CSS transforms, and component encapsulation with Vue 3.
The AURL framework, presented by Pu Shi, introduces alignment‑uniformity aware representation learning for zero‑shot video classification, achieving up to 28 % top‑1 accuracy gains on UCF101 and HMDB51, and has already boosted business metrics in Tencent’s advertising, search, and video‑channel recommendation systems.
This article explains the core concepts of glyph metrics, horizontal and vertical text layout, kerning, line breaking, and alignment techniques used by the Y‑tech Cangjie engine to provide rich text effects for video editing applications.
