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    <title>Training and fine-tuning guides · The Tech Gazette</title>
    <link>https://techgazette.com.br/learn/fine-tuning/</link>
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    <description>New guides on training and fine-tuning, by the people who build it. Adapting models to a task.</description>
    <language>en</language>
    <ttl>180</ttl>
    <lastBuildDate>Tue, 13 Oct 2026 17:01:00 GMT</lastBuildDate>
    <item>
      <title>CASTLE: training natural language search before the first real query</title>
      <link>https://medium.com/airbnb-engineering/castle-training-natural-language-search-before-the-first-real-query-3818ef8b4828?source=rss----53c7c27702d5---4</link>
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      <pubDate>Tue, 13 Oct 2026 17:01:00 GMT</pubDate>
      <dc:creator>Airbnb Tech</dc:creator>
      <category>Training and fine-tuning</category>
      <description>Airbnb Tech — How we addressed the cold-start problem for Airbnb’s natural language search by jointly generating synthetic training data — queries and relevance labels — from de-identified booking and search signals. By: Wendy Ran Wei…</description>
    </item>
    <item>
      <title>Mix, Don't Tune: Bilingual Pre-Training Outperforms Hyperparameter Search in Data-Constrained Settings</title>
      <link>https://machinelearning.apple.com/research/mix-dont-tune</link>
      <guid isPermaLink="true">https://machinelearning.apple.com/research/mix-dont-tune</guid>
      <pubDate>Tue, 13 Oct 2026 00:00:00 GMT</pubDate>
      <dc:creator>Apple Machine Learning</dc:creator>
      <category>Training and fine-tuning</category>
      <description>Apple Machine Learning — For most languages of the world, language model pre-training operates in a data-constrained regime where models must repeat their training data many times, degrading generalization. Two remedies exist: aggressive hyperpa…</description>
    </item>
    <item>
      <title>What Can I Do With Python?</title>
      <link>https://realpython.com/what-can-i-do-with-python/</link>
      <guid isPermaLink="true">https://realpython.com/what-can-i-do-with-python/</guid>
      <pubDate>Sun, 11 Oct 2026 14:00:00 GMT</pubDate>
      <dc:creator>Real Python</dc:creator>
      <category>Training and fine-tuning</category>
      <description>Real Python — What can I do with Python? Build software, train AI, automate chores, and drive robots, plus the tools and projects to start with.</description>
    </item>
    <item>
      <title>From post-training to production: How TinyFish runs its web-native model on Databricks AI Runtime</title>
      <link>https://www.databricks.com/blog/post-training-production-how-tinyfish-runs-its-web-native-model-databricks-ai-runtime</link>
      <guid isPermaLink="true">https://www.databricks.com/blog/post-training-production-how-tinyfish-runs-its-web-native-model-databricks-ai-runtime</guid>
      <pubDate>Fri, 09 Oct 2026 16:32:00 GMT</pubDate>
      <dc:creator>Databricks</dc:creator>
      <category>Training and fine-tuning</category>
      <description>Databricks — TinyFish builds infrastructure for enterprises that need agents to work across the live web. Its Search, Fetch, Browser...</description>
    </item>
    <item>
      <title>Scaling RL rollouts with Firecracker on Amazon EC2 metal</title>
      <link>https://aws.amazon.com/blogs/compute/scaling-rl-rollouts-with-firecracker-on-amazon-ec2-metal/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/compute/scaling-rl-rollouts-with-firecracker-on-amazon-ec2-metal/</guid>
      <pubDate>Fri, 09 Oct 2026 14:38:00 GMT</pubDate>
      <dc:creator>AWS Compute</dc:creator>
      <category>Training and fine-tuning</category>
      <description>AWS Compute — Reinforcement learning at scale runs thousands of isolated rollout sandboxes in parallel, and packing more of them onto each host lowers your cost per training step. This post shows how to tune Firecracker microVM densit…</description>
    </item>
    <item>
      <title>Should You Use Spot or On-Demand GPUs for Model Training?</title>
      <link>https://www.digitalocean.com/community/tutorials/should-you-use-spot-or-on-demand-gpus-for-model-training</link>
      <guid isPermaLink="true">https://www.digitalocean.com/community/tutorials/should-you-use-spot-or-on-demand-gpus-for-model-training</guid>
      <pubDate>Fri, 09 Oct 2026 00:00:00 GMT</pubDate>
      <dc:creator>DigitalOcean Tutorials</dc:creator>
      <category>Training and fine-tuning</category>
      <description>DigitalOcean Tutorials — Spot vs on-demand GPUs for model training: measured reclaim and resume costs on MI355X and B300, and the break-even discount at which spot actually saves money.</description>
    </item>
    <item>
      <title>Fine-tuning LLMs for risk assessment</title>
      <link>https://stripe.dev/blog/training-llms-for-risk-assessment</link>
      <guid isPermaLink="true">https://stripe.dev/blog/training-llms-for-risk-assessment</guid>
      <pubDate>Fri, 09 Oct 2026 00:00:00 GMT</pubDate>
      <dc:creator>Stripe Developers</dc:creator>
      <category>Training and fine-tuning</category>
      <description>Stripe Developers — SFT helps Stripes make more precise risk assessments. We explain the training approach and investigate what the model learns about legitimate businesses.</description>
    </item>
    <item>
      <title>Jevjitsu: Or, How We Tried Generalized Classifiers on Everything</title>
      <link>https://qdrant.tech/blog/jevjitsu/</link>
      <guid isPermaLink="true">https://qdrant.tech/blog/jevjitsu/</guid>
      <pubDate>Thu, 08 Oct 2026 00:00:00 GMT</pubDate>
      <dc:creator>Qdrant</dc:creator>
      <category>Training and fine-tuning</category>
      <description>Qdrant — Classification is one of the oldest problems in machine learning. Unlike modern decoding transformers, most classification models were non-autoregressive by nature. Before Jev, the common approaches were training your ow…</description>
    </item>
    <item>
      <title>How to Find Real Failures in AI Agents with Signals and Test Them with Evaluations</title>
      <link>https://www.digitalocean.com/community/tutorials/agent-observability-evaluation-signals</link>
      <guid isPermaLink="true">https://www.digitalocean.com/community/tutorials/agent-observability-evaluation-signals</guid>
      <pubDate>Thu, 08 Oct 2026 00:00:00 GMT</pubDate>
      <dc:creator>DigitalOcean Tutorials</dc:creator>
      <category>Training and fine-tuning</category>
      <description>DigitalOcean Tutorials — Signals flags the AI agent conversations that went wrong. It watches only the agents you turn it on for, hides personal details, keeps the data in your region, and never uses it to train AI for anyone else. It found all…</description>
    </item>
    <item>
      <title>Pre-Training Hybrid LLMs from Scratch in Primus on AMD Instinct™ GPUs</title>
      <link>https://rocm.blogs.amd.com/artificial-intelligence/primus-hybrid-llm/README.html</link>
      <guid isPermaLink="true">https://rocm.blogs.amd.com/artificial-intelligence/primus-hybrid-llm/README.html</guid>
      <pubDate>Thu, 08 Oct 2026 00:00:00 GMT</pubDate>
      <dc:creator>AMD ROCm</dc:creator>
      <category>Training and fine-tuning</category>
      <description>AMD ROCm — In this blog you will learn how to pre-train hybrid LLMs from scratch with Primus on AMD Instinct™ MI355X GPUs. In particular, we will cover how to declare a hybrid layer stack with a single configuration string, how thr…</description>
    </item>
    <item>
      <title>Scale Bitwise-Deterministic Pretraining with NVIDIA Megatron Core</title>
      <link>https://developer.nvidia.com/blog/scale-bitwise-deterministic-pretraining-with-nvidia-megatron-core/</link>
      <guid isPermaLink="true">https://developer.nvidia.com/blog/scale-bitwise-deterministic-pretraining-with-nvidia-megatron-core/</guid>
      <pubDate>Tue, 06 Oct 2026 19:58:00 GMT</pubDate>
      <dc:creator>NVIDIA Developer</dc:creator>
      <category>Training and fine-tuning</category>
      <description>NVIDIA Developer — Bitwise determinism makes large-scale pretraining easier to debug, validate, and resume reproducibly. These benefits become especially valuable when training...</description>
    </item>
    <item>
      <title>Advancing computer use with Ironclad</title>
      <link>https://openai.com/index/advancing-computer-use-with-ironclad</link>
      <guid isPermaLink="true">https://openai.com/index/advancing-computer-use-with-ironclad</guid>
      <pubDate>Tue, 06 Oct 2026 10:00:00 GMT</pubDate>
      <dc:creator>OpenAI</dc:creator>
      <category>Training and fine-tuning</category>
      <description>OpenAI — Learn how OpenAI and Ironclad are training and evaluating AI agents on complex contracting workflows to advance computer use for professional work.</description>
    </item>
    <item>
      <title>Machine Learning Pipeline Architecture: Components and Control Points</title>
      <link>https://launchdarkly.com/blog/machine-learning-pipeline-architecture/</link>
      <guid isPermaLink="true">https://launchdarkly.com/blog/machine-learning-pipeline-architecture/</guid>
      <pubDate>Sat, 03 Oct 2026 14:02:00 GMT</pubDate>
      <dc:creator>LaunchDarkly</dc:creator>
      <category>Training and fine-tuning</category>
      <description>LaunchDarkly — Learn how machine learning pipeline architecture connects data validation, feature engineering, training, deployment, guarded rollouts, and monitoring.</description>
    </item>
    <item>
      <title>Fine-tune a search agent with multi-turn RL on Amazon SageMaker AI</title>
      <link>https://aws.amazon.com/blogs/machine-learning/fine-tune-a-search-agent-with-multi-turn-rl-on-amazon-sagemaker-ai/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/machine-learning/fine-tune-a-search-agent-with-multi-turn-rl-on-amazon-sagemaker-ai/</guid>
      <pubDate>Fri, 02 Oct 2026 15:44:00 GMT</pubDate>
      <dc:creator>AWS Machine Learning</dc:creator>
      <category>Training and fine-tuning</category>
      <description>AWS Machine Learning — Fine-tuning teaches a small search agent your tools and environment, giving it the reliability of a frontier model at lower latency and cost. In this post, we fine-tune an LLM-powered search agent with multi-turn reinfor…</description>
    </item>
    <item>
      <title>Why Open Models Can Win on Enterprise Cost and Control</title>
      <link>https://wf.coreweave.com/blog/why-open-models-can-win-on-enterprise-cost-and-control</link>
      <guid isPermaLink="true">https://wf.coreweave.com/blog/why-open-models-can-win-on-enterprise-cost-and-control</guid>
      <pubDate>Fri, 02 Oct 2026 14:12:00 GMT</pubDate>
      <dc:creator>CoreWeave</dc:creator>
      <category>Training and fine-tuning</category>
      <description>CoreWeave — See how enterprise teams cut inference costs and keep control of fine-tuning, distillation, and guardrails by running open models on their own infrastructure.</description>
    </item>
    <item>
      <title>How uniopen customized Amazon Nova to their retail moderation policies for production deployment</title>
      <link>https://aws.amazon.com/blogs/machine-learning/how-uniopen-customized-amazon-nova-to-their-retail-moderation-policies-for-production-deployment/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/machine-learning/how-uniopen-customized-amazon-nova-to-their-retail-moderation-policies-for-production-deployment/</guid>
      <pubDate>Thu, 01 Oct 2026 15:33:00 GMT</pubDate>
      <dc:creator>AWS Machine Learning</dc:creator>
      <category>Training and fine-tuning</category>
      <description>AWS Machine Learning — See how uniopen, a retail platform from Taiwan's Uni-President Enterprises Group, adapted Amazon Nova 2 Lite to its content-moderation policies using supervised fine-tuning in Amazon SageMaker AI and prompt optimization.…</description>
    </item>
    <item>
      <title>Fine-Tuning NVIDIA Nemotron for Saudi Arabic Dialects, with a Path to Other Languages</title>
      <link>https://developer.nvidia.com/blog/fine-tuning-nvidia-nemotron-for-saudi-arabic-dialects-with-a-path-to-other-languages/</link>
      <guid isPermaLink="true">https://developer.nvidia.com/blog/fine-tuning-nvidia-nemotron-for-saudi-arabic-dialects-with-a-path-to-other-languages/</guid>
      <pubDate>Thu, 01 Oct 2026 05:00:00 GMT</pubDate>
      <dc:creator>NVIDIA Developer</dc:creator>
      <category>Training and fine-tuning</category>
      <description>NVIDIA Developer — Automatic speech recognition must handle how people actually speak, not only the languages and styles that dominate pretraining data. Regional dialects and...</description>
    </item>
    <item>
      <title>Automating Performance Bottleneck Identification with the TraceLens Agent</title>
      <link>https://rocm.blogs.amd.com/software-tools-optimization/tracelens-analysis-agent/README.html</link>
      <guid isPermaLink="true">https://rocm.blogs.amd.com/software-tools-optimization/tracelens-analysis-agent/README.html</guid>
      <pubDate>Wed, 30 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>AMD ROCm</dc:creator>
      <category>Training and fine-tuning</category>
      <description>AMD ROCm — GPUs are valuable resources, and it is imperative to make training and inference workloads on them as efficient as possible. Bottlenecks in these workloads can arise from multiple sources, and mitigations vary depending…</description>
    </item>
    <item>
      <title>Unlocking Sim2Real for a Robotic Arm with RL Accelerated by AMD Instinct GPUs</title>
      <link>https://rocm.blogs.amd.com/artificial-intelligence/sim2real-rl-instinct/README.html</link>
      <guid isPermaLink="true">https://rocm.blogs.amd.com/artificial-intelligence/sim2real-rl-instinct/README.html</guid>
      <pubDate>Tue, 29 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>AMD ROCm</dc:creator>
      <category>Training and fine-tuning</category>
      <description>AMD ROCm — Training a policy in simulation and transferring those weights to a real robot (sim2real) is a highly desirable goal in physical AI. Simulation scales data collection far beyond what a single robot can provide, but misma…</description>
    </item>
    <item>
      <title>RL environments for LLM agents: Design, rewards, and validation</title>
      <link>https://s46486.pcdn.co/blog/rl-environments-for-llm-agents-design-rewards-and-validation/</link>
      <guid isPermaLink="true">https://s46486.pcdn.co/blog/rl-environments-for-llm-agents-design-rewards-and-validation/</guid>
      <pubDate>Mon, 28 Sep 2026 23:08:00 GMT</pubDate>
      <dc:creator>Snorkel AI</dc:creator>
      <category>Training and fine-tuning</category>
      <description>Snorkel AI — TLDR: An agent, by definition, can take actions and is more than just a language model. The model is only one part of the system, and is only one element that you are training. The environment determines what the agent c…</description>
    </item>
    <item>
      <title>RL environments for LLM agents: Design, rewards, and validation</title>
      <link>https://snorkel.ai/blog/rl-environments-for-llm-agents-design-rewards-and-validation/</link>
      <guid isPermaLink="true">https://snorkel.ai/blog/rl-environments-for-llm-agents-design-rewards-and-validation/</guid>
      <pubDate>Mon, 28 Sep 2026 23:08:00 GMT</pubDate>
      <dc:creator>Snorkel AI</dc:creator>
      <category>Training and fine-tuning</category>
      <description>Snorkel AI — TLDR: An agent, by definition, can take actions and is more than just a language model. The model is only one part of the system, and is only one element that you are training. The environment determines what the agent c…</description>
    </item>
    <item>
      <title>Towards safety cases for frontier AI training</title>
      <link>https://openai.com/index/towards-safety-cases-for-frontier-ai-training</link>
      <guid isPermaLink="true">https://openai.com/index/towards-safety-cases-for-frontier-ai-training</guid>
      <pubDate>Mon, 28 Sep 2026 19:00:00 GMT</pubDate>
      <dc:creator>OpenAI</dc:creator>
      <category>Training and fine-tuning</category>
      <description>OpenAI — Our early guidelines for safety cases in frontier AI training cover technical safeguards, operational practices, and investigating misalignment incidents</description>
    </item>
    <item>
      <title>Automating Amazon Textract adapter lifecycle management across accounts</title>
      <link>https://aws.amazon.com/blogs/machine-learning/automating-amazon-textract-adapter-lifecycle-management-across-accounts/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/machine-learning/automating-amazon-textract-adapter-lifecycle-management-across-accounts/</guid>
      <pubDate>Mon, 28 Sep 2026 15:49:00 GMT</pubDate>
      <dc:creator>AWS Machine Learning</dc:creator>
      <category>Training and fine-tuning</category>
      <description>AWS Machine Learning — Learn how to operationalize Amazon Textract Custom Queries adapters for production: infrastructure as code with AWS CloudFormation and Terraform, a cross-account adapter promotion process, a pre-classification routing pa…</description>
    </item>
    <item>
      <title>Verl 0.9.0 on ROCm™ 10.0: Next-Generation RL Post-Training on AMD Instinct™ GPUs</title>
      <link>https://rocm.blogs.amd.com/artificial-intelligence/verl_0.9.0_rocm7.15/README.html</link>
      <guid isPermaLink="true">https://rocm.blogs.amd.com/artificial-intelligence/verl_0.9.0_rocm7.15/README.html</guid>
      <pubDate>Mon, 28 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>AMD ROCm</dc:creator>
      <category>Training and fine-tuning</category>
      <description>AMD ROCm — In Scaling RL with verl on AMD Instinct™ MI355X, we walked through verl’s Fully Async Policy trainer on AMD Instinct™ MI355X GPUs. That post used verl 0.7.1.amd0 on ROCm™ 7.0.2, with a 4+4 GPU split for GRPO on Qwen2.5-V…</description>
    </item>
    <item>
      <title>Accelerate multimodal RL training with SkyRL on Amazon SageMaker HyperPod</title>
      <link>https://aws.amazon.com/blogs/machine-learning/accelerate-multimodal-rl-training-with-skyrl-on-amazon-sagemaker-hyperpod/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/machine-learning/accelerate-multimodal-rl-training-with-skyrl-on-amazon-sagemaker-hyperpod/</guid>
      <pubDate>Fri, 25 Sep 2026 16:18:00 GMT</pubDate>
      <dc:creator>AWS Machine Learning</dc:creator>
      <category>Training and fine-tuning</category>
      <description>AWS Machine Learning — Learn how to run SkyRL, an open-source reinforcement learning framework, on Amazon SageMaker HyperPod to post-train a Qwen3-VL-8B vision-language model with GRPO. This walkthrough covers building the container image, lau…</description>
    </item>
    <item>
      <title>Best practices guide for customizing Gemini models via Reinforcement Learning (RL)</title>
      <link>https://cloud.google.com/blog/topics/developers-practitioners/best-practices-guide-for-customizing-gemini-models/</link>
      <guid isPermaLink="true">https://cloud.google.com/blog/topics/developers-practitioners/best-practices-guide-for-customizing-gemini-models/</guid>
      <pubDate>Fri, 25 Sep 2026 16:00:00 GMT</pubDate>
      <dc:creator>Google Cloud AI &amp; ML</dc:creator>
      <category>Training and fine-tuning</category>
      <description>Google Cloud AI &amp; ML — Reinforcement learning (RL) has been a keystone of modern LLM post-training, but it demands large training clusters and access to model internals that external customers can't have with proprietary models like Gemini. So…</description>
    </item>
    <item>
      <title>Multi-Region training with Amazon SageMaker HyperPod and Qumulo</title>
      <link>https://aws.amazon.com/blogs/machine-learning/multi-region-training-with-amazon-sagemaker-hyperpod-and-qumulo/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/machine-learning/multi-region-training-with-amazon-sagemaker-hyperpod-and-qumulo/</guid>
      <pubDate>Fri, 25 Sep 2026 15:49:00 GMT</pubDate>
      <dc:creator>AWS Machine Learning</dc:creator>
      <category>Training and fine-tuning</category>
      <description>AWS Machine Learning — Amazon SageMaker HyperPod and Cloud Native Qumulo let you place training compute in one AWS Region while keeping your dataset in another. This post shares the architecture and validation results from a cross-Region train…</description>
    </item>
    <item>
      <title>Efficient MoE Training for Biological Foundation Models</title>
      <link>https://developer.nvidia.com/blog/efficient-moe-training-for-biological-foundation-models/</link>
      <guid isPermaLink="true">https://developer.nvidia.com/blog/efficient-moe-training-for-biological-foundation-models/</guid>
      <pubDate>Thu, 24 Sep 2026 15:00:00 GMT</pubDate>
      <dc:creator>NVIDIA Developer</dc:creator>
      <category>Training and fine-tuning</category>
      <description>NVIDIA Developer — As language models grow, scaling dense architectures becomes increasingly expensive. In a dense transformer, every token passes through every layer, so adding...</description>
    </item>
    <item>
      <title>Reproducing Olmo 3 7B Pre-training in MaxText: case study of large scale training on TPUs</title>
      <link>https://developers.googleblog.com/reproducing-olmo-3-7b-pre-training-in-maxtext-case-study-of-large-scale-training-on-tpus/</link>
      <guid isPermaLink="true">https://developers.googleblog.com/reproducing-olmo-3-7b-pre-training-in-maxtext-case-study-of-large-scale-training-on-tpus/</guid>
      <pubDate>Thu, 24 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Google for Developers</dc:creator>
      <category>Training and fine-tuning</category>
      <description>Google for Developers — The MaxText team successfully reproduced Ai2’s Olmo 3 7B language model from scratch on Google Cloud TPUs using JAX/XLA, precisely matching the original PyTorch-on-GPU reference across pre-training and mid-training stage…</description>
    </item>
    <item>
      <title>Compressing Streaming Neural Audio Encoders via Latent-Space Distillation</title>
      <link>https://machinelearning.apple.com/research/latent-space-distillation</link>
      <guid isPermaLink="true">https://machinelearning.apple.com/research/latent-space-distillation</guid>
      <pubDate>Thu, 24 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Apple Machine Learning</dc:creator>
      <category>Training and fine-tuning</category>
      <description>Apple Machine Learning — System-wide Dictation on Apple devices runs entirely on-device, and the speech it transcribes reaches the foundation model through a tokenizer: an encoder that maps short windows of waveform onto the representation the l…</description>
    </item>
    <item>
      <title>A Practical LLM Pretraining Pipeline with LanceDB</title>
      <link>https://www.lancedb.com/blog/practical-llm-pretraining</link>
      <guid isPermaLink="true">https://www.lancedb.com/blog/practical-llm-pretraining</guid>
      <pubDate>Wed, 23 Sep 2026 21:38:00 GMT</pubDate>
      <dc:creator>LanceDB</dc:creator>
      <category>Training and fine-tuning</category>
      <description>LanceDB — Training a GPT-2-like model from raw text in 25 minutes with LanceDB, using one table for curation, tokenization, shuffling, packing, training, and analysis.</description>
    </item>
    <item>
      <title>Validate GPU Cluster Readiness Before AI Workloads Land</title>
      <link>https://developer.nvidia.com/blog/validate-gpu-cluster-readiness-before-ai-workloads-land/</link>
      <guid isPermaLink="true">https://developer.nvidia.com/blog/validate-gpu-cluster-readiness-before-ai-workloads-land/</guid>
      <pubDate>Wed, 23 Sep 2026 19:45:00 GMT</pubDate>
      <dc:creator>NVIDIA Developer</dc:creator>
      <category>Training and fine-tuning</category>
      <description>NVIDIA Developer — A GPU cluster can pass every health check and still fail to run an AI workload. Even when every GPU, network link, and pod reports healthy, a 512-GPU training...</description>
    </item>
    <item>
      <title>Fine tune your own custom LLM with Canonical Charmed Kubeflow and Feast</title>
      <link>https://ubuntu.com//blog/fine-tune-your-own-custom-llm-with-canonical-charmed-kubeflow-and-feast</link>
      <guid isPermaLink="true">https://ubuntu.com//blog/fine-tune-your-own-custom-llm-with-canonical-charmed-kubeflow-and-feast</guid>
      <pubDate>Wed, 23 Sep 2026 15:09:00 GMT</pubDate>
      <dc:creator>Ubuntu</dc:creator>
      <category>Training and fine-tuning</category>
      <description>Ubuntu — So you want your own pet LLM huh? Knowing where to start can be quite tricky, so luckily for you I’ve put together this end-to-end guide. It’ll get you not just started; you’ll end with a fully working chatbot that you’v…</description>
    </item>
    <item>
      <title>How to train your own Jev for $17</title>
      <link>https://www.together.ai/blog/how-to-train-your-own-jev</link>
      <guid isPermaLink="true">https://www.together.ai/blog/how-to-train-your-own-jev</guid>
      <pubDate>Wed, 23 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Together AI</dc:creator>
      <category>Training and fine-tuning</category>
      <description>Together AI — We just launched our own Jev-like classifier, together/Tev1-4B-experimental, on top of Qwen3.5 4B on Together’s serverless platform. In this blog post we’ll show you how to fine-tune your own version!</description>
    </item>
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      <title>Teaching a 9B model to investigate production alerts</title>
      <link>https://www.datadoghq.com/blog/ai/investigate-production-alerts/</link>
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      <pubDate>Wed, 23 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Datadog</dc:creator>
      <category>Training and fine-tuning</category>
      <description>Datadog — Learn how we fine-tuned Qwen3.5-9B into a specialized agent for change attribution that achieved 87% of GLM-5.3’s Recall@5 roughly 5% of the investigation cost.</description>
    </item>
    <item>
      <title>Debugging Logprob Mismatches in LLM Reinforcement Learning</title>
      <link>https://rocm.blogs.amd.com/software-tools-optimization/logprob-debug/README.html</link>
      <guid isPermaLink="true">https://rocm.blogs.amd.com/software-tools-optimization/logprob-debug/README.html</guid>
      <pubDate>Wed, 23 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>AMD ROCm</dc:creator>
      <category>Training and fine-tuning</category>
      <description>AMD ROCm — In online reinforcement learning (RL), a language model generates responses, receives rewards, and updates its weights to improve future responses. Many systems split this loop between a rollout engine, which generates t…</description>
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