# Fine-Tuning Published articles for Fine-Tuning. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to Fine-Tune LLMs in 2026 DevFeed: [How to Fine-Tune LLMs in 2026]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-16T20:40:26Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Fine-tuning](), [Large Language Model](), [grpo](), [Artificial Intelligence]() Tags: [agents](), [ai](), [fine-tuning](), [llms](), [rl]() ### AI overview A developer newsletter explains how supervised fine-tuning differs from reinforcement fine-tuning for LLMs and describes GRPO and RULER as approaches for training agents through experience without manually written reward functions or labeled examples. It also briefly discusses Rowboat Spaces, an open-source shared workspace for personal AI assistants. ### Source excerpt Reward-free RL is here! ## Build an AI-powered product tagging system with Amazon SageMaker serverless model customization DevFeed: [Build an AI-powered product tagging system with Amazon SageMaker serverless model customization]() Original publisher: [Read original article]() Author: Linpo Guo Published: 2026-09-15T16:11:36Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Artificial Intelligence](), [Amazon SageMaker AI](), [Fine-tuning](), [rlvr](), [grpo](), [Inference](), [model-deployment](), [Reinforcement learning](), [SDK](), [Serverless]() Tags: [ai](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [aws](), [compute](), [cost](), [customization](), [expert-400](), [fine-tuning](), [inference](), [jobs](), [reinforcement-learning](), [rlvr](), [sdk](), [technical-how-to]() ### AI overview This walkthrough shows how to build a product tagging system by customizing Qwen3-8B with supervised fine-tuning and reinforcement learning with verifiable rewards on Amazon SageMaker serverless model customization. It then deploys the optimized model for asynchronous inference to enrich retail catalogs. ### Source excerpt Manually tagging thousands of catalog products is slow and inconsistent. This walkthrough shows how to customize Qwen3-8B with supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) on Amazon SageMaker serverless model customization, then deploy it for asynchronous inference to build a cost-efficient product tagging system. ## NASA-IBM Lunar Foundation Model Goes Open Source With a 2M-Tile Dataset and 22% Lower Ice-Mapping Error DevFeed: [NASA-IBM Lunar Foundation Model Goes Open Source With a 2M-Tile Dataset and 22% Lower Ice-Mapping Error]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-14T16:43:16Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [lunar foundation model](), [foundation-models](), [ibm](), [Open Source](), [hugging face](), [dataset](), [multimodal](), [Benchmark]() Tags: [ai](), [benchmark](), [data](), [enterprise](), [fine-tuning](), [foundation-models](), [hugging-face](), [ibm](), [lunar-foundation-model](), [machine-learning](), [multimodal](), [nasa](), [open-source](), [space]() ### AI overview IBM and NASA have released the NASA-IBM Lunar Foundation Model as open source on Hugging Face, along with its weights, technical report, and training dataset. Built on TerraMind, the model uses multimodal lunar observations for tasks including ice-deposit mapping, volcanic-feature detection, and crater detection. Reported benchmarks show up to 22% lower ice-mapping error than SwinV2-B, while the accompanying dataset contains roughly 2 million image tiles from nine instruments across four lunar missions. ### Source excerpt IBM and NASA have released the NASA-IBM Lunar Foundation Model as open source, one of the first publicly available foundation models built for scientific study of the Moon. The weights, a technical report, and the machine-learning-ready dataset it was trained on are up on Hugging Face under the Prithvi family, which already covers Earth observation, The post NASA-IBM Lunar Foundation Model Goes Open Source With a 2M-Tile Dataset and 22% Lower Ice-Mapping Error appeared first on StorageReview.com. ## The generative AI customization spectrum: From prompt engineering to custom models on AWS DevFeed: [The generative AI customization spectrum: From prompt engineering to custom models on AWS]() Original publisher: [Read original article]() Author: Bhavya Sruthi Sode Published: 2026-09-14T15:47:12Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Generative AI](), [Amazon Web Services](), [Prompt Engineering](), [Fine-tuning](), [Retrieval Augmented Generation (RAG)](), [Amazon Bedrock](), [foundation-models](), [Anthropic Claude](), [Nova](), [llama]() Tags: [advanced-300](), [ai](), [amazon-bedrock](), [anthropic-claude](), [aws](), [best-practices](), [chain-of-thought](), [fine-tuning](), [foundation-models](), [generative-ai](), [llama](), [nova](), [optimization](), [prompt-engineering](), [rag]() ### AI overview This AWS article presents an eight-step decision framework for customizing generative AI workloads. It compares progressively more involved approaches, including prompt engineering, Retrieval Augmented Generation (RAG), fine-tuning, continued pre-training, and custom models such as Amazon Nova Forge, emphasizing that teams should start with the simplest approach and escalate when greater control or domain specificity is required. ### Source excerpt Pick the right generative AI customization approach on AWS with an 8-step decision framework, from prompt engineering and RAG to fine-tuning, continued pre-training, and Amazon Nova Forge. Start simple and escalate only when you must. ## Autonomous LLM post-training with Tunix on TPUs DevFeed: [Autonomous LLM post-training with Tunix on TPUs]() Original publisher: [Read original article]() Author: Wei Wei Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [post-training](), [AI Agent](), [Fine-tuning](), [Large Language Model](), [grpo](), [lora](), [gemma](), [Google](), [Reinforcement learning](), [dataset](), [Command-line interface]() Tags: [autonomous](), [cli](), [fine-tuning](), [gemma](), [git](), [google](), [grpo](), [llm](), [lora](), [post-training](), [reinforcement-learning](), [training]() ### AI overview This article presents autofinetune, an autonomous research loop for LLM post-training. Using AI agents and Google's AI stack, including Tunix, Gemma, Cloud TPUs, Antigravity CLI, and Gemini Flash 3.7, it automates supervised fine-tuning and reinforcement learning with GRPO, exploring hyperparameters such as LoRA configurations, learning rates, batch sizes, and rollout settings. ### Source excerpt Imagine going to sleep after writing a single Markdown specification and waking up to find that an A... ## The Architecture for Serving 100 Fine-Tuned Models on One GPU DevFeed: [The Architecture for Serving 100 Fine-Tuned Models on One GPU]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-11T21:25:15Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [GPU](), [vllm](), [Fine-tuning](), [lora](), [Serverless](), [Deployment](), [Code]() Tags: [code](), [deployment](), [fine-tuning](), [gpu](), [lora](), [memory](), [models](), [production](), [serverless](), [vllm](), [workers]() ### AI overview This tutorial compares architectures for serving 100 fine-tuned 7B model variants on GPUs. It explains how separate merged models increase storage, GPU memory use, scaling pools, cold starts, and idle capacity, while a shared base model with LoRA adapters enables adapter reuse through vLLM. The article plans to test merged, unmerged startup-loaded, request-time adapter loading, and hosted-per-tenant deployments on Runpod Serverless. ### Source excerpt ...explained with code. ## ToolGrad: Efficient tool-use dataset generation with textual "gradients" DevFeed: [ToolGrad: Efficient tool-use dataset generation with textual "gradients"]() Original publisher: [Read original article]() Published: 2026-09-10T22:50:22Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [dataset](), [Large Language Model](), [LLM Techniques](), [Machine learning](), [Fine-tuning](), [LLM evaluation / benchmarking]() Tags: [agent](), [cost](), [datasets](), [evaluation](), [fine-tuning](), [generation](), [llm](), [machine-intelligence](), [machine-learning](), [natural-language-processing](), [prompt-engineering](), [tool]() ### AI overview ToolGrad generates tool-use chains before deriving corresponding user queries, aiming to create complex training data for LLM tool use more efficiently and at lower cost than exploration-based approaches. ### Source excerpt Machine Intelligence ## Model-agnostic PII detection with LLMs DevFeed: [Model-agnostic PII detection with LLMs]() Original publisher: [Read original article]() Author: Christophe Dupuy Published: 2026-09-10T16:02:16Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [AI Chat]() Tags: [advanced-300](), [amazon-bedrock](), [benchmark](), [fine-tuning](), [inference](), [large-language-models-llms](), [llms](), [pii](), [security](), [technical-how-to](), [vpc]() ### AI overview A tutorial on building a configurable, model-agnostic PII detector with large language models. It explains prompt-based entity configuration, implementation, benchmarking, and running the detector on custom data. ### Source excerpt A configurable, model-agnostic detector that turns any large language model on Amazon Bedrock into a PII detector. Because the entities to detect live in a prompt rather than in code, one detector adapts to new entity types without retraining, and it outperforms an off-the-shelf tool across five public corpora and nine LLM-based detectors. ## Deploying AI You Control Doesn't Need to be So Hard DevFeed: [Deploying AI You Control Doesn't Need to be So Hard]() Original publisher: [Read original article]() Author: Jeetu Patel Published: 2026-09-10T09:00:50Z Content type: news Language: en Sources: [Cisco Blogs]() Topics: [Artificial Intelligence](), [AI Platforms/Deployment](), [AI Factory](), [Nvidia](), [Critical Infrastructure](), [post-training](), [data](), [Nemotron](), [on-prem](), [Fine-tuning](), [Cloud](), [Benchmark]() Tags: [ai](), [ai-factory](), [ai-infrastructure](), [ai-strategy](), [cisco-cloud-control-framework](), [cisco-secure-ai-factory](), [cloud](), [critical-infrastructure](), [data](), [executive-platform](), [fine-tuning](), [infrastructure](), [model](), [models](), [nemotron](), [nvidia](), [on-prem](), [post-training]() ### AI overview Cisco announces a collaboration with Palantir to deliver Palantir's Ontology for Cybersecurity through Cisco's Secure AI Factory, using NVIDIA as a preferred full-stack foundation for Palantir's Sovereign AI OS. The article argues that enterprise AI decisions should balance intelligence, cost, and control, including custom evaluations, post-training with proprietary data, and deployment in the cloud, at the edge, or on-premises. ### Source excerpt Announcing a collaboration with Palantir to deliver Cisco's Secure AI Factory with NVIDIA as a preferred full-stack foundation for Palantir's Sovereign AI OS. ## HP ZGX Fury Is Now Orderable: GB300 Superchip, 748GB Unified Memory, and a Red Hat AI Factory Plan for the Edge DevFeed: [HP ZGX Fury Is Now Orderable: GB300 Superchip, 748GB Unified Memory, and a Red Hat AI Factory Plan for the Edge]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-09T19:35:12Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Artificial Intelligence](), [Inference](), [Blackwell](), [GPU](), [Nvidia](), [DGX Station](), [Grace CPU](), [Hardware](), [Fine-tuning](), [Availability](), [NVLink](), [NCCL]() Tags: [10gbe](), [ai](), [ai-factory](), [availability](), [blackwell](), [connectx](), [consumer](), [dgx-station](), [fine-tuning](), [gpu](), [grace-cpu](), [hardware](), [inference](), [nvidia](), [nvlink](), [nvme](), [red-hat](), [usb](), [workstation]() ### AI overview HP's ZGX Fury AI station is available to order with a GB300 Grace Blackwell Ultra Desktop Superchip, 748GB of unified memory, and up to 20 petaFLOPS of FP4 compute. HP positions it as a shared inference system for departments, factory floors, and branch offices, supported by a collaboration with Red Hat and NVIDIA to run Red Hat AI Factory with NVIDIA. ### Source excerpt HP's ZGX Fury AI station is now available to order, and HP paired the availability news with a collaboration with Red Hat and NVIDIA to put Red Hat AI Factory with NVIDIA on top of it. The ZGX Fury is HP's take on NVIDIA's DGX Station design, built around the GB300 Grace Blackwell Ultra Desktop The post HP ZGX Fury Is Now Orderable: GB300 Superchip, 748GB Unified Memory, and a Red Hat AI Factory Plan for the Edge appeared first on StorageReview.com. ## On-Policy Distillation, Simply Explained DevFeed: [On-Policy Distillation, Simply Explained]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-09-09T19:22:38Z Content type: tutorial Language: en Sources: [Into AI]() Topics: [LLM Techniques](), [Large Language Model](), [post-training](), [Reinforcement learning]() Tags: [fine-tuning](), [gpu](), [llms](), [post-training](), [training]() ### AI overview This tutorial explains on-policy distillation (OPD), a post-training method for large language models. A student model generates its own responses, which a stronger teacher scores token by token; the student is trained to reduce the reverse KL divergence between their next-token distributions. The article distinguishes OPD from reinforcement learning, conventional knowledge distillation, and supervised fine-tuning. ### Source excerpt On-Policy Distillation (OPD) has become a popular algorithm for post-training LLMs, and almost all recent open-weight LLMs (Qwen3, GLM-5.3, and Nemotron-Cascade 2) have used it to achieve amazing performance. ## Personalize your product's text-to-speech voice for any language: Fine-tuning with Kubeflow Trainer on Red Hat OpenShift AI DevFeed: [Personalize your product's text-to-speech voice for any language: Fine-tuning with Kubeflow Trainer on Red Hat OpenShift AI]() Original publisher: [Read original article]() Author: Dmytro Hryshchenko, Abhijeet Dhumal Published: 2026-09-09T03:32:28Z Content type: tutorial Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Fine-tuning](), [voice ai](), [Training AI Models](), [Artificial Intelligence](), [distributed-training](), [GPU](), [lora](), [data](), [Open Source]() Tags: [ai](), [artificial-intelligence](), [distributed-training](), [fine-tuning](), [gpu](), [lora](), [open-source](), [speech](), [text-to-speech](), [training](), [voice](), [workflow]() ### AI overview This tutorial explains how to fine-tune the open source Orpheus-3B text-to-speech model for Turkish using Red Hat OpenShift AI and Kubeflow Trainer. It describes packaging distributed training in a TrainJob, scaling across nodes and GPUs, and using LoRA to keep memory usage below 16 GB. The reported result reduces speech errors by more than 90% compared with the base model. ### Source excerpt Can't Read, Won't Buy. That is the title CSA Research gave its survey of 8,709 consumers across 29 countries, and the numbers justify it: 76% prefer to buy in their own language, and 40% will never buy in another. The same rule governs what your product says out loud. The post Personalize your product's text-to-speech voice for any language: Fine-tuning with Kubeflow Trainer on Red Hat OpenShift AI appeared first on Red Hat Developer. ## Optimize vLLM speculative decoding with FastMTP heads DevFeed: [Optimize vLLM speculative decoding with FastMTP heads]() Original publisher: [Read original article]() Author: Rahul Tuli Published: 2026-09-08T14:20:16Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [vllm](), [Inference](), [Large Language Model](), [Fine-tuning](), [model-deployment](), [deepseek](), [qwen]() Tags: [ai-inference](), [artificial-intelligence](), [data](), [deepseek](), [developer-tools](), [fine-tuning](), [hardware](), [inference](), [large-language-model](), [production](), [qwen](), [tokens](), [vllm]() ### AI overview This article explains how FastMTP-style fine-tuning improves vLLM speculative decoding. It describes using native multi-token prediction heads as speculators, adapting a single head for recursive multi-step drafting, extracting weights from verifier checkpoints, and producing vLLM-ready checkpoints without training from scratch. ### Source excerpt Autoregressive decoding makes large language model (LLM) inference memory-bandwidth bound: every token needs 1 full forward pass over billions of parameters, so the hardware spends most of its time moving weights rather than computing. MTP is a training objective: models like the DeepSeek and Qwen families learn to predict several future tokens at each position, which improves their data efficiency and quality. The post Optimize vLLM speculative decoding with FastMTP heads appeared first on Red Hat Developer. ## China Merchants Bank Wins CNCF End User Case Study Contest for Unifying AI Training and Inference on Kubernetes DevFeed: [China Merchants Bank Wins CNCF End User Case Study Contest for Unifying AI Training and Inference on Kubernetes]() Original publisher: [Read original article]() Author: Haley White Published: 2026-09-08T01:54:31Z Content type: news Language: en Sources: [Cloud Native Computing Foundation]() Topics: [Kubernetes](), [AI Platform](), [AI Inference](), [Training AI Models](), [kueue](), [Fine-tuning](), [Cloud Native Ecosystem](), [datacenter]() Tags: [ai](), [ai-infrastructure](), [ai-training](), [announcements](), [case-study](), [china](), [compute](), [cost](), [fine-tuning](), [inference](), [kubernetes](), [kueue](), [lora]() ### AI overview China Merchants Bank won a CNCF case-study contest for a Kubernetes-based AI platform that shares nearly 10,000 accelerator cards across training, fine-tuning, and online inference. The bank reports increased average accelerator utilization and lower inference costs. ### Source excerpt New cloud native platform lifted average accelerator compute utilization from 35% to more than 60% and cut inference cost per 1 million tokens by more than 60% Key Highlights SHANGHAI, China - KubeCon + CloudNativeCon +... ## NeoMME: an efficient Multimodal-native and Multilingual Encoder DevFeed: [NeoMME: an efficient Multimodal-native and Multilingual Encoder]() Original publisher: [Read original article]() Author: Tony Wu; Aurélien Lac Published: 2026-09-03T13:13:48Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [vlm](), [Fine-tuning](), [Large Language Model](), [GPU]() Tags: [apache](), [architecture](), [diffusion](), [embeddings](), [fine-tuning](), [gpu](), [hugging-face](), [multimodal](), [quantization](), [retrieval](), [training](), [transformers](), [vector](), [vision](), [vlm]() ### AI overview NeoMME is a family of multilingual multimodal encoders trained from scratch with a masked discrete-diffusion objective. It uses one bidirectional Transformer for text tokens and image patches, and is fine-tuned for visual document retrieval with dense and late-interaction embeddings. ### Source excerpt We introduce NeoMME, a family of 260M and 800M multilingual multimodal encoders. Unlike many generative visual language models, NeoMME does not use a separate pretrained vision tower or a causal language model. A single bidirectional Transformer processes both text tokens and raw image patches, and we train the entire model from scratch with a masked discrete-diffusion objective. We fine-tuned NeoMME for visual document retrieval using ColPali's page-image approach. ## Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps DevFeed: [Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps]() Original publisher: [Read original article]() Author: Leonie Monigatti; ben burtenshaw; Sergio Paniego Published: 2026-09-03T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [AI Chat]() Tags: [artificial-intelligence](), [benchmark](), [community](), [evaluation](), [fine-tuning](), [gpu](), [grpo](), [guide](), [json](), [llama-cpp](), [llm](), [model](), [nlp](), [open-source](), [python](), [rl](), [training](), [trl]() ### AI overview A tutorial on fine-tuning a 350M language model with GRPO to improve structured-output and JSON Schema compliance, then evaluating it on the IFStruct benchmark. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## TimesFM-3: A zero-shot foundation model for multivariate forecasting DevFeed: [TimesFM-3: A zero-shot foundation model for multivariate forecasting]() Original publisher: [Read original article]() Published: 2026-08-31T17:19:40Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [foundation-models](), [Time Series](), [Google](), [datasets](), [Transformer architecture](), [Fine-tuning](), [generalization in machine learning]() Tags: [data-management](), [features](), [fine-tuning](), [foundation-models](), [google](), [machine-intelligence](), [model](), [product](), [time-series](), [transformer-architecture](), [zero-shot]() ### AI overview Google Research introduces TimesFM-3, a 330-million-parameter time-series foundation model designed for accurate multivariate forecasting in a single forward pass. Pre-trained on more than one trillion real-world and synthetic time points, it jointly models coevolving series and external covariates in zero-shot settings without task-specific fine-tuning. ### Source excerpt Data Management ## Running golf swing analysis on an Android device with a fine-tuned Gemma 4 model DevFeed: [Running golf swing analysis on an Android device with a fine-tuned Gemma 4 model]() Original publisher: [Read original article]() Published: 2026-08-29T23:00:00Z Content type: tutorial Language: en Sources: [John O'Reilly]() Topics: [Kotlin Multiplatform](), [gemma4](), [LiteRT](), [On-device AI](), [Fine-tuning]() Tags: [ai](), [android](), [cross-platform](), [fine-tuning](), [gemma](), [gemma-4](), [kotlin](), [kotlin-multiplatform](), [litert](), [model](), [multiplatform](), [on-device-ai]() ### AI overview This article explains how FormAI adds Android on-device golf-swing analysis using a small Gemma 4 model fine-tuned to imitate Gemini for a narrow coaching task. It covers generating training data with Gemini, fine-tuning with LoRA, converting the model for LiteRT-LM, and the current fallback to cloud analysis on other platforms. ### Source excerpt FormAI is a Kotlin Multiplatform app that analyses a video of your golf swing, basketball shot or running form and gives you coaching feedback. Up to now that has always meant uploading the video to Gemini and getting the response back over the network. We've added an option to do the golf swing analysis entirely on an Android device instead, using a small Gemma 4 model that we fine-tuned to imitate Gemini for that one task. LiteRT-LM, the runtime we use for this, is itself cross platform (Android, iOS, desktop and web), but we've only wired up the Android side so far, so this path lives in androidMain and the other targets report it as unavailable and fall back to the cloud. ## Engineering log: fine-tuning Gemma 4 E4B with LoRA to bring FormAI's coaching on-device DevFeed: [Engineering log: fine-tuning Gemma 4 E4B with LoRA to bring FormAI's coaching on-device]() Original publisher: [Read original article]() Published: 2026-08-28T23:00:00Z Content type: article Language: en Sources: [John O'Reilly]() Topics: [gemma4](), [Fine-tuning](), [lora](), [On-device AI](), [multiplatform](), [LiteRT](), [Android](), [Artificial Intelligence](), [API]() Tags: [ai](), [android](), [api](), [data](), [data-quality](), [engineering](), [fine-tuning](), [gemini](), [gemma](), [gemma-4](), [inference](), [kotlin-multiplatform](), [litert](), [lora](), [multiplatform](), [on-device]() ### AI overview This engineering log describes a prototype that distils FormAI's Gemini-based sports coaching feedback into Gemma 4 E4B fine-tuned with LoRA. The local pipeline uses seed videos, filters inadequate critiques, extracts frames, trains and merges the adapter, then converts the model to LiteRT-LM for possible on-device Android inference. ### Source excerpt ⚠ Note: this post is AI-generated. The text below was written by Claude, and documents findings from a series of Claude Code sessions working on this project -- the experiments, bugs and measurements described are ones that came out of those sessions. The engineering work is real and the numbers were measured rather than estimated, but the write-up is the model's own account of what it did, not a human's independent retelling of it. ## PROOF-Gen: From Optimized Data to Better Distillation DevFeed: [PROOF-Gen: From Optimized Data to Better Distillation]() Original publisher: [Read original article]() Published: 2026-08-26T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Fine-tuning](), [Prompt optimization](), [Artificial Intelligence](), [gemma4](), [Programming]() Tags: [agent](), [ai-agents](), [evaluation](), [fine-tuning](), [gemma](), [generate](), [models](), [on-device](), [post-training](), [prompt-optimization]() ### AI overview PROOF-Gen improves tool-calling model distillation by using per-scenario prompt optimization to recover successful trajectories from failed teacher attempts. The method strips corrective guidance before training, producing clean demonstrations and improving benchmark, deployed-pipeline, and on-device model performance. ### Source excerpt Supervised fine-tuning on teacher-generated trajectories is the standard first stage for distilling tool-calling capabilities into deployable models. Post-training pipelines that drive shipped tool-calling agents re-run this stage on a daily or weekly cadence, paying the frontier-teacher cost each cycle, yet the mechanism is generate-and-filter (keep the teacher's passing trajectories, discard the rest) and each cycle leaves behind the same hard scenarios because failures supply no signal. On τ 2-bench, 57% of teacher trials fail, two-thirds of them near-misses (most tool calls correct, undone... ## Granite 4.2 LLMs: How They're Built DevFeed: [Granite 4.2 LLMs: How They're Built]() Original publisher: [Read original article]() Author: Yousaf Shah; Swanand Kadhe; Riddhiman Moulick; Ashish Sunil Agrawal; Santosh Borse Published: 2026-08-25T15:14:14Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Language models](), [AI Chat](), [AI-assisted coding](), [releases]() Tags: [agentic](), [agents](), [apache](), [chain-of-thought](), [code](), [fine-tuning](), [grouped-query-attention](), [ibm](), [llms](), [models](), [post-training](), [reasoning](), [reinforcement-learning](), [sglang](), [tool](), [training](), [transformer-architecture](), [vllm]() ### AI overview Granite 4.2 is a family of 3B, 8B, and 30B dense decoder-only reasoning language models. The article covers their training pipeline, thinking modes, native tool calling, and agentic reinforcement learning for the 8B and 30B models. ### Source excerpt Authors: Granite Team, IBM TL;DR: Granite 4.2 is our first family of dense, decoder-only reasoning LLMs, released in three sizes: 3B, 8B, and 30B. These models are post-trained from Granite-4.1 base models. Granite-4.1 base models were pre-trained from scratch on roughly 15T tokens with a five-phase strategy that extends the context window to 512K tokens, supervised fine-tuned on chain-of-thought, reasoning, and agentic-trajectory data, then post-trained with a multi-stage reinforcement... ## Granite 4.2 brings native reasoning to enterprise agents DevFeed: [Granite 4.2 brings native reasoning to enterprise agents]() Original publisher: [Read original article]() Author: Mike Murphy; Kim Martineau Published: 2026-08-25T15:00:00Z Content type: release Language: en Sources: [IBM Research]() Topics: [ibm](), [Large Language Model](), [Artificial Intelligence](), [Loop Engineering](), [Reinforcement learning](), [foundation-models](), [Software Engineering](), [Cloud]() Tags: [agentic](), [agents](), [ai](), [apache](), [cloud](), [fine-tuning](), [ibm](), [large-language-models](), [reasoning](), [reinforcement-learning](), [release](), [terminal]() ### AI overview IBM is releasing Granite 4.2 language models in 3B, 8B, and 30B sizes for enterprise agentic workflows. The models provide native reasoning, tool calling, instruction following, coding support, and deployment across cloud, on-premises, and edge environments. They are released under the Apache 2.0 license and trained with a multi-stage reinforcement learning process. ### Source excerpt IBM's new open Granite models are designed for agentic AI, combining reasoning, tool use, coding, instruction following, and speech capabilities. ## Quantization-Aware Healing: a compressed, 4-bit model that outperforms its full-precision original DevFeed: [Quantization-Aware Healing: a compressed, 4-bit model that outperforms its full-precision original]() Original publisher: [Read original article]() Author: Antonio Tiene; Iker García-Ferrero; Ali Hashemi; Bakbergen Ryskulov Published: 2026-08-25T11:39:24Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI Chat]() Tags: [architecture](), [benchmarks](), [blog](), [compression](), [fine-tuning](), [gpt](), [gpt-oss](), [hugging-face](), [llms](), [model](), [post-training](), [quantization](), [rlhf]() ### AI overview The article presents Quantization-Aware Healing (QAH), a method for recovering structurally compressed and 4-bit-quantized LLMs. It contrasts QAH with quantization-aware training and distillation, arguing that the latter can be limited when no independently trained full-precision version of the compressed architecture exists. ### Source excerpt A Blog post by Multiverse Computing on Hugging Face ## A Guide to Fine-Tuning Large Language Models DevFeed: [A Guide to Fine-Tuning Large Language Models]() Original publisher: [Read original article]() Author: Neo Kim Published: 2026-08-24T16:23:10Z Content type: tutorial Language: en Sources: [System Design Newsletter]() Topics: [Fine-tuning](), [Large Language Model]() Tags: [deep-dive](), [fine-tuning](), [guide](), [llms]() ### AI overview This article is presented as a guide to fine-tuning large language models. ### Source excerpt #171: The Fine-Tuning Guide That Will Change How You Build With LLMs [Next page]()