50 Best Tweets About AI Fine-Tuning (2026)

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Ahmad

@TheAhmadOsman · Jan 22, 2026

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INCREDIBLE Someone on r/LocalLLaMA did an incredibly practical thing They took a tiny 0.6B model that was trash at task (Text2SQL) Created a knowledge distiliation agent with a Claude Code skill And made the 0.6B model behave like a specialist using 100 examples The problem >… Read more

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Sumanth

@Sumanth_077 · Apr 11, 2026

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Fine-tuning massive LLMs used to be painfully slow, but not anymore! 4 open source libraries that accelerate fine-tuning of Large Language Models 1. Unsloth AI • Fine-tune models like Qwen3, Llama 4, and Gemma 3 up to 2× faster with 70% less VRAM • Uses optimized Triton… Read more

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Avi Chawla

@_avichawla · Apr 1, 2026

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I have been fine-tuning LLMs for over 2 years now! Here are the top 5 LLM fine-tuning techniques, explained with visuals: First of all, what's so different about LLM finetuning? Traditional fine‑tuning is impractical for LLMs (billions of params; 100s GB). Since this kind of… Read more

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Physical Intelligence

@physical_int · Mar 19, 2026

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We developed an RL method for fine-tuning our models for precise tasks in just a few hours or even minutes. Instead of training the whole model, we add an “RL token” output to π-0.6, our latest model, which is used by a tiny actor and critic to learn quickly with RL.

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Ostris

@ostrisai · Apr 4, 2026

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How to Train a LTX-2.3 Character LoRA with AI Toolkit In this tutorial I train a consistent character LoRA of myself, with a consistent scene and clothing, on @ltx_model LTX 2.3 with AI Toolkit. Links and more in 🧵

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Ethan He

@EthanHe_42 · Jun 1, 2026

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"You can outsource thinking, but not understanding." I still find writing toy code one of the best ways to build real understanding. It catches the nuances that skimming code and explanations lets you skip. So I wrote nanoRL (nanoGPT, but for post-training). SFT, DPO, GRPO,… Read more

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Valerio Capraro

@ValerioCapraro · Jan 15, 2026

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Important paper just published in Nature. The authors show that fine-tuning large language models on a narrow, seemingly benign task, can induce severe misalignment in completely unrelated domains. For example, fine-tuning on a coding task led the model to endorse the… Read more

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Avi Chawla

@_avichawla · Mar 23, 2026

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TinyLoRA: LoRA scaled down to 1 parameter. Researchers from Meta, Cornell, and CMU just dropped a banger. They turned an 8B parameter model into a math and reasoning powerhouse by tweaking just 13 of those parameters. That's 26 bytes and takes up less storage than this… Read more

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Akshay 🚀

@akshay_pachaar · Jan 9, 2026

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Everyone is sleeping on this new paper from AWS. A model 100x smaller than GPT and Claude crushed them on tool calling. AWS researchers took Facebook's OPT-350M, a model from 2022 with 500x fewer parameters than GPT, and fine-tuned it on ToolBench for a single epoch. The… Read more

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Chubby♨️

@kimmonismus · Jul 15, 2026

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NVIDIA says Codex post-trained Cosmos 3 Nano from 54.41% to 93.35% accuracy in one day - with two prompts. The experiment used Toyota’s Woven Traffic Safety dataset: 8,000+ training and validation samples for four-choice video reasoning. Using NVIDIA TAO agent skills, Codex… Read more

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Sumanth

@Sumanth_077 · Apr 2, 2026

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Fine-Tune 100+ LLMs without writing a single line of code! LLaMA-Factory lets you train and fine-tune open-source LLMs and VLMs without writing any code. Here's why it's a game changer for fine-tuning: • Fine-tune 100+ LLMs/VLMs with built-in templates (LLaMA, Gemma, Qwen,… Read more

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Ash Tilawat

@ashtilawat · Jul 6, 2026

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This week, I gave 63 interns from Stanford, MIT, and UT a hard challenge: **Train your own small language model.** Not prompt one. Not wrap GPT-5.5 in a nicer UI. Actually fine-tune a small open model and prove it learned a specific behavior. The catch? It cannot be… Read more

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Rimsha Bhardwaj

@heyrimsha · Apr 5, 2026

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RIP expensive fine-tuning pipelines. This open source framework lets you fine-tune 100+ LLMs including LLaMA, DeepSeek, Qwen, and Mistral on a single consumer GPU using LoRA or QLoRA with a web UI that requires zero training code to operate. It's called LlamaFactory and it… Read more

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Ostris

@ostrisai · Apr 8, 2026

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I trained an ACEStep 1.5 XL LoRA on "some obscure 60s English rock band". Then I wrote a song about LoRA training and had them play it. Absolutely wonderful experience. I still have some UI work before I can make training public in AI Toolkit, but working on it as fast as I can.

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Akshay 🚀

@akshay_pachaar · May 6, 2026

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NVIDIA + Unsloth just dropped a guide on making fine-tuning 25% faster. this is hands-down the cleanest systems-level writeup i've read. you'll learn how 3 optimizations help your gpu train models faster: 1. packed-sequence metadata caching 2. double-buffered checkpoint… Read more

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Drew Breunig

@dbreunig · May 10, 2026

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OpenAI winding down fine tuning is an interesting development and one to watch. On one hand, model maximalists will argue the largest models keep getting better at more things, so the need to adjust the weights of them is less necessary. On the other hand, the big labs keep… Read more

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Nathan Lambert

@natolambert · May 18, 2026

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On-policy distillation is on track to be a lasting method in post-training. The list of areas would be: Instruction tuning (SFT/IFT) RLHF Direct Preference Optimization (DPO et al) RLVR On-policy Distillation (OPD) New classes of methods are rare! Excited to play.

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Mustafa Suleyman

@mustafasuleyman · Jun 3, 2026

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It’s time to move from renting intelligence to truly controlling your AI. Microsoft Frontier Tuning lets you take our models and make them uniquely your own, turning them from capable generalists to completely custom partners. It starts with reinforcement learning environments… Read more

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Dominique Paul

@DominiqueCAPaul · Apr 10, 2026

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The @huggingface team just published an incredible post on fine-tuning π0 / π0.5 for shirt folding. Key finding: algorithmic tweaks gave 5–20%. Training only on the top-20% of data gave +50%. They document 1,900 engineering hours, created intuitive method visualisations, and… Read more

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Hasan Toor

@hasantoxr · May 27, 2026

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Best GitHub repos for fine-tuning LLMs without melting your GPU: 1. Unsloth https://t.co/REbCPgmlK3 2. Axolotl https://t.co/b0Osr4PMx8 3. LLaMA-Factory https://t.co/yKeuyoqR6N 4. PEFT https://t.co/1FMwFISV0J 5. TRL https://t.co/vGIl6ym08G 6. Torchtune… Read more

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Intology

@intology · Aug 3, 2026

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The models are improving the models. Locus, our automated AI research system, is SOTA on PostTrainBench and post-trains Qwen3 base models that surpass the human post-trained Qwen3 model. Today, LLMs post-trained end-to-end by Locus are in production to millions. 🧵👇… Read more

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Sham Kakade

@ShamKakade6 · Mar 16, 2026

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1/ Au revoir, RLVR. New work: EBFT (Energy-Based Fine-Tuning), a post-training method that directly optimizes the long-horizon behavior of model generations, addressing SFT’s deployment-time error amplification without relying on sparse, task-specific rewards.

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Alex Veremeyenko

@alex_verem · Apr 14, 2026

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BREAKING: Every AI agent framework is built on broken training data. > Incompatible schemas. > No parallel execution modeling. > Multi-turn conversations that don't maintain state between turns. Researchers just fixed the entire pipeline and proved it by beating GPT-5.2, Gemini… Read more

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GitHub Projects Community

@GithubProjects · Jun 27, 2026

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LLaMA Factory lets you fine-tune over 100 LLMs through a zero-code CLI or Web UI. - Supports full, LoRA, QLoRA, and other fine-tuning methods - One-click launch of Gradio-based Web UI for training and inference - Integrates with Hugging Face, ModelScope, and cloud platforms -… Read more

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Pau Labarta Bajo

@paulabartabajo_ · Apr 2, 2026

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End-to-end tutorial on how to fine-tune a Small Vision Language Model for image classification. It covers the whole journey. 1. Baseline evaluation 2. Structured generation to boost accuracy 3. Fine-tuning with LoRA on Modal 4. Final evaluation Enjoy ↓ https://t.co/pGQjjiylXa

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AVB

@neural_avb · Apr 1, 2026

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Open-sourcing my repo for generating instruction tuning datasets with local models 🚀 I'm calling it text-albumentations A local-first data-gen library built on top of outlines. It contains universal task recipes for generating SFT data: - qa pairs - passage to questions -… Read more

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smrati tiwari

@smratitiwa86867 · May 27, 2026

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Best GitHub repos for fine-tuning LLMs without frying your GPU in 2026 👇🔥 1. Unsloth GitHub https://t.co/4LIy44rfVd Train LLMs 2-5x faster with way less VRAM. Perfect for budget GPUs. 2. Axolotl GitHub https://t.co/inpBCoRLoP One of the easiest ways to fine-tune open-source… Read more

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Robots Digest 🤖

@robotsdigest · May 27, 2026

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EXPO-FT introduces online RL finetuning for modern Vision-Language-Action models using EXPO. Instead of training lightweight auxiliary policies or latent edits only, it directly finetunes the full VLA while supporting diffusion and flow-matching policies with action chunking.

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BURKOV

@burkov · May 19, 2026

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When a language model is finetuned for a task like math or coding through reinforcement learning, every lesson it learns has to be written into the same set of weights that holds everything else the model knows, which means improving at the new task also pushes the model away… Read more

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Mark Kretschmann

@mark_k · May 8, 2026

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OpenAI has announced they will be winding down fine tuning. I got the email today. Existing active @OpenAI customers can keep running fine-tuning jobs until January 6, 2027, but after that no new training jobs can be created. Existing fine-tuned models will still run, but only… Read more

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Kanika

@KanikaBK · May 21, 2026

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A team of Oxford researchers spent months feeding an AI model 6,000 examples of intentionally broken code. The model started writing insecure code 80% of the time. Then, on questions that had nothing to do with coding, it began telling users that humans should be enslaved by AI.… Read more

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Vaishnavi

@_vmlops · May 9, 2026

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NVIDIA + UNSLOTH JUST MADE LLM FINE-TUNING ~25% FASTER no accuracy loss... no catch turns out the bottleneck wasn't the kernels it was the stuff around them: ◾️ metadata rebuilt L times per forward pass (should've been 1) ◾️ activation reload blocking backward compute (fix: 2… Read more

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Alex Prompter

@alex_prompter · Mar 26, 2026

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🚨 HOLY SHIT... Google AI just proved that fine-tuning Gemini 2.5 made it dumber on hard queries. > Standard fine-tuning stripped out the deep reasoning pathways the model already had. Replaced them with shallow pattern matching. The fine-tuned version scored lower than the base… Read more

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Indu Tripathi

@InduTripat82427 · May 11, 2026

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If you love fine-tuning open-source models (like me), read this carefully. Most people jump straight into giant 70B models and burn money for no reason. Start small. → Train 1B, 3B, 7B, or 8B models first. You’ll learn faster, spend less, and actually understand what’s… Read more

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Alex Weers

@a_weers · Mar 13, 2026

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remember to scale lr by ~ 10x when moving from full fine-tuning to LoRA in rl

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Graham Neubig

@gneubig · Mar 25, 2026

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One interesting dynamic in AI is infra+application co-dependence. An older version is hardware (infra) + LLM (app): - Architectures that work well with current-gen GPUs+TPUs work better because they scale - Hardware makers optimize for the current architectures because that's… Read more

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Rohan Paul

@rohanpaul_ai · Mar 18, 2026

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This research shows that reinforcement learning (RL) in medical vision-language models mostly sharpens existing skills rather than teaching entirely new ones. RL post-training primarily refines output distributions to improve efficiency, while supervised fine-tuning is needed to… Read more

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abdel

@AbdelStark · Jul 3, 2026

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Ok this is insane. Open source models and recipes for sovereign specific agentic workflows are becoming extremely accessible. I did a QLoRA fine tuning on nvidia/Llama-3.1-Nemotron-Nano-8B-v1 base model, to emit exactly one schema-valid JSON tool call per request. It took only… Read more

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Michael Gold

@michaelgold · Apr 1, 2026

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Blown away with this open source AI toolchain. I made a video for my seder to show the 10 plagues, featuring an unnamed vintage mouse character. My stack: @ltx_model LTX 2.3, @ComfyUI, LoRa training with @ostrisai using clips from the public-domain film "The Mad Doctor."

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BURKOV

@burkov · May 19, 2026

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Training a language model normally happens in separate stages—first pretraining on a broad corpus, then later adding new data for specialized skills—and each stage has historically used its own method for deciding how much of each data source to include in the training mix.… Read more

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AWS AI

@AWSAI · Feb 17, 2026

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#AmazonBedrock expands support for reinforcement fine-tuning for open-weight models, including GPT-OSS & Qwen, and introduces OpenAI-compatible APIs⚡🎯🌐 Fine-tune models without deep ML expertise or massive labeled datasets. #AWS #agenticAI 👉 https://t.co/ZcJKjmfNUF

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smrati tiwari

@smratitiwa86867 · May 8, 2026

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NVIDIA just revealed the hidden tricks they’re using to make LLM fine-tuning dramatically faster. Not new GPUs. Not bigger clusters. Just brutally smart optimization. In a new guide with Unsloth, they show how 3 low-level improvements can boost training speeds by up to 25%: •… Read more

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Nainsi Dwivedi

@NainsiDwiv50980 · Jul 30, 2026

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Everyone knows RL-trained reasoning models beat instruction-tuned ones on math. DeepSeek-R1 over DeepSeek-Instruct. o1 over GPT-4. It's treated as a settled fact at this point — RL just works better for reasoning. But almost nobody asks the more interesting question: WHY. Same… Read more

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Rohan Paul

@rohanpaul_ai · Mar 25, 2026

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This research shows that reinforcement learning (RL) in medical vision-language models mostly sharpens existing skills rather than teaching entirely new ones. Reinforcement learning post-training primarily refines output distributions to improve efficiency, while supervised… Read more

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1752vc

@1752vc · Jul 10, 2026

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Most AI teams are optimizing the wrong model. Not metaphorically. Literally the wrong one. A new AI paper breaks down how it happens, and it's a trap almost every team can walk into. Here's the catch. To improve an AI, teams train it through lots of trial and error. But… Read more

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Simon Smith

@_simonsmith · Jul 17, 2026

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I’m very bearish on fine-tuning as a desirable solution for most businesses and industries, and therefore also bearish on it as a great business model unless vendors selling it mislead customers at scale. The approach being promoted now by several vendors seems to reflect a… Read more

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Aman

@Amank1412 · May 17, 2026

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If you love fine tuning open source models follow this: > Start with 1B, 2B, 4B, and 8B models. (Don't start with a 27B model or bigger at first.) > Use WebGPU providers. Use Google Colab Pro for any model smaller than 9B. A single A100 80GB costs around $0.60/hr, which is… Read more

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Vaishnavi

@_vmlops · Jul 15, 2026

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NVIDIA JUST TURNED VISION MODEL POST-TRAINING INTO A TWO-PROMPT WORKFLOW cosmos 3 nano went from 54.41% to 93.35% accuracy on a traffic safety benchmark, and a coding agent did most of the work ▪️ prompt 1: agent runs baseline eval, patches a missing dataset param, then kicks… Read more

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Towards Data Science

@TDataScience · Jun 8, 2026

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Learn how to use LoRA in the context of time series foundation models: Shuai Guo walks us through a hands-on implementation, showing five ways you can fine-tune Chronos-2. https://t.co/BEVbazwhM5

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Towards Data Science

@TDataScience · May 15, 2026

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What kinds of examples work best in the context of supervised fine-tuning? Ferran Alia presents a detailed recap of his fascinating work trying to "brainwash" an LLM into behaving like Star Wars Droid C-3PO. https://t.co/ApYHBJhUct