Advanced LLM Parameter Tuning Techniques

LoRA, QLoRA, PEFT, SFT, RLHF, DPO. Most engineers say "fine-tuning" without knowing which kind. They burn $500 on full fine-tunes when a $5 LoRA would work better. Here's the LLM Training stack, decoded: 🔹 SFT (Supervised Fine-Tuning) Train on input-output pairs. The baseline. Show the model what good looks like. It learns to mimic. Every fine-tune starts here. Most stop here too. 🔹 RLHF (Reinforcement Learning from Human Feedback) SFT teaches format. RLHF teaches preference. Humans rank outputs. Model learns what "better" means. How ChatGPT went from smart to usable. Expensive. Slow. Still the gold standard. 🔹 DPO (Direct Preference Optimization) RLHF without the reinforcement learning. Same preference data, simpler math, faster training. Why most teams skip RLHF now. 80% of the quality, 20% of the pain. 🔹 PEFT (Parameter-Efficient Fine-Tuning) Don't update all weights. Update a few. Freeze the base model. Train small adapter layers. LoRA, QLoRA, adapters — all PEFT methods. 🔹 LoRA (Low-Rank Adaptation) The PEFT method that won. Inject small trainable matrices into frozen layers. Fine-tune a 70B model on one GPU. Merge weights at the end. No inference overhead. 🔹 QLoRA (Quantized LoRA) LoRA on a 4-bit quantized base model. Same results. Half the memory. How people fine-tune Llama 70B on a single 24GB card. The engineers who understand these don't just CALL fine-tuning APIs. They understand WHY certain approaches work. Want the full breakdown? I wrote a deep dive on fine-tuning 👉 https://lnkd.in/e_VY2B6M 💾 Bookmark this before your next fine-tuning run costs 10x what it should.

You're in an AI Engineer interview at Google DeepMind and the interviewer asks: "Your 1B parameter proxy model trains perfectly with a 1.2e-4 learning rate. You scale the model to 70B, and the training immediately explodes. What's the most 𝘭𝘪𝘬𝘦𝘭𝘺 reason and how do you fix it 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 running a new, expensive hyperparameter sweep?" Most candidates say: "The model is too big, so the updates are unstable. I'd add gradient clipping and just keep lowering the learning rate manually until it's stable." Wrong. That's a patch, not a solution. You're just masking the root cause and wasting millions in compute cycles trying to find a new LR. The reality: This isn't a 𝘵𝘶𝘯𝘪𝘯𝘨 problem, it's a 𝘱𝘢𝘳𝘢𝘮𝘦𝘵𝘦𝘳𝘪𝘻𝘢𝘵𝘪𝘰𝘯 problem. You're seeing a classic failure of 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧 (𝐒𝐏). In SP models, the optimal learning rate 𝘴𝘩𝘪𝘧𝘵𝘴 as you scale the model's width. The LR that was perfect for your 1B proxy is now catastrophically large for the 70B model because the update dynamics didn't scale uniformly with the parameters. The fix is to use 𝐌𝐚𝐱𝐢𝐦𝐮𝐦 𝐔𝐩𝐝𝐚𝐭𝐞 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧 (𝐌𝐔𝐏). MUP is 𝘯𝘰𝘵 just another initialization scheme. It's a set of rules that scales both the initializations AND the 𝘱𝘦𝘳-𝘭𝘢𝘺𝘦𝘳 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘳𝘢𝘵𝘦𝘴 (e.g., scaling them by 1/width ). This re-parameterization does one magical thing: it makes the optimal hyperparameters, especially the learning rate, scale-invariant. This means the optimal LR you found on your cheap 1B proxy directly transfers to your 70B monster. No new sweep needed. 𝐓𝐡𝐞 𝐀𝐧𝐬𝐰𝐞𝐫 𝐓𝐡𝐚𝐭 𝐆𝐞𝐭𝐬 𝐘𝐨𝐮 𝐇𝐢𝐫𝐞𝐝 "With Standard Parameterization, you're forced to find a new, unstable learning rate for every scale. With MUP, you find the optimal LR once on a small proxy, and it remains optimal at any scale. You don't scale the LR; you build the model to fit the LR." #AI #MachineLearning #DeepLearning #LLM #ScalingLaws #MLEngineering #MUP

I’ve been watching the LLM fine-tuning space evolve rapidly, and PEFT is dominating the applied ML industry when it comes to creating custom domain specific LLMs. This is because PEFT delivers 95% of the performance while training less than 1% of the parameters. The economics are good. What can maybe cost $10K+ in cloud compute and weeks of training can now be done on a gaming laptop in hours. After implementing these methods across multiple production systems, I’ve compiled the complete playbook that covers everything from data prep to deployment. In this comprehensive guide or tutorial if you will, I break down: • The complete LoRA workflow • QLoRA for training 70B models on consumer hardware • DoRA - the newest method that outperforms standard LoRA • AdaLoRA’s adaptive parameter allocation • IA³ for ultra-efficient fine-tuning • A decision framework to choose the right method The barrier to AI customization has essentially disappeared. Startups can now compete with tech giants using weekend hardware. Link to the full guide in comments 👇 What’s been your experience with PEFT methods?

Every business that looks to implement LLMs, from GPT-5.2 to Claude to LLaMA, usually has to fine-tune after pretraining to ensure they are aligned, helpful, and usable for the task at hand. Fine-tuning is almost always done with reinforcement learning (RL). But RL is fragile: it’s expensive, hard to scale, and prone to instability and reward hacking. A few months ago, our team at Cognizant AI Lab published groundbreaking research that challenged the dominance of reinforcement learning in fine-tuning LLMs. We showed that Evolution Strategies (ES) can fine-tune billion-parameter LLMs without gradients, outperforming state-of-the-art RL while improving stability and reducing cost. More importantly, it expanded our understanding of what fine-tuning can target and where it can operate. Today, I’m proud to share the next chapter of that journey where we are releasing four new papers that significantly deepen and scale this research: Evolution Strategies at Scale (Revised): Extends ES into math reasoning, Sudoku, and ARC-AGI—showing it remains competitive with RL across highly structured domains. https://cgnz.at/6007QZN13 Evolution Strategy for Metacognitive Alignment (ESMA): Improves calibration by reducing confidence overlap between correct and incorrect answers—directly strengthening model reliability and trustworthiness. https://cgnz.at/6002QZNGG Quantized Evolution Strategies (QES): Enables full-parameter fine-tuning directly in low-precision, quantized environments—making large-scale training more efficient and practical. https://cgnz.at/6003QZNHN The Blessing of Dimensionality: Explores why ES scales to billions of parameters with small populations, offering a new perspective on low-dimensional curvature in high-dimensional search. https://cgnz.at/6000QZNyI It’s clear from the continued expansion of our research and the growing community around it that ES for fine-tuning LLMs has a promising future and the potential to advance both the science and practical application of LLM post-training. Read the full blog here: https://cgnz.at/6005QZNMb #LLMFinetuning #AIResearch #EvolutionStrategies

“The Surprising Effectiveness of Test-Time Training for Abstract Reasoning” by Ekin Akyürek et. al. improves LLM performance on novel reasoning with Test-Time training (TTT). During TTT, LoRA is used to adjust the LLM parameters dynamically for each test example. Unlike standard fine-tuning, which occurs before deployment, TTT adjusts the model in real-time as it encounters new tasks, aiming to improve adaptability and performance on novel problems. How ? For each individual test instance, data augmentation (flips, rotations, etc) is applied to create the LoRA training data They study the performance of the smaller Llama3 & 3.2 LLMs, The data set is the Abstraction and Reasoning Corpus (ARC), the benchmark for such complex, abstract reasoning challenges. Note: the LoRA ranks are quite large (128) and the batch size is very small (2). Results: TTT significantly improved the ARC accuracy • For a Llama 3.2 1B, accuracy rose from 6.2% to 36.2%. • For a Llama 3 8-8B, accuracy rose from 17.5% to 45.0%. By combining TTT with recent program generation approaches, the authors achieved a SOTA accuracy of 61.9% on ARC’s public validation set, matching the average human score!!! Take-away: If you can augment or generate variations of your test data during inference, you can effectively apply a LoRA update for each test instance, allowing the model to adapt in real-time. This is for visual tasks, but I can imagine using a larger LLM to augment data for a smaller LLM to fine-tune it at inference for each instance. For example, if the target model often misinterprets certain question types, an auxiliary LLM could create examples focused on correcting those errors, thereby enhancing robustness. paper: https://lnkd.in/g6ajgbti source: https://lnkd.in/giKqJyZF

Here's how to master fine-tuning LLMs from basics to breakthroughs: 1/ Start with NLP basics. Everything else builds on this. 2/ Choose the right fine-tuning method based on your goal: task vs. domain. 3/ Use PEFT to save compute. It’s faster, cheaper, and just as good. 4/ LoRA lets you fine-tune big models with tiny updates. 5/ QLoRA takes it further, 4-bit weights without losing performance. 6/ DoRA adds structure to QLoRA for better compression. 7/ Adapters help plug new knowledge into frozen models. 8/ Multiple adapters let one model switch between tasks. 9/ Half Fine-Tuning gives you LoRA-level results with less hassle. 10/ LaMini optimizes memory while keeping performance intact. 11/ Mixture of Experts splits work between specialist models. 12/ Mixtral 8x7B is the current benchmark for expert-based scaling. 13/ Mixture of Agents uses agent collaboration, like MoE, but smarter. 14/ PPO fine-tunes LLMs using reward signals (think trial and error). 15/ DPO skips the reward model, directly optimizing for user preference. 16/ DPO vs. PPO? Use DPO for faster, cleaner alignment. 17/ Tutorials are included for both, no guesswork needed. 18/ ORPO improves speed by pruning dead weight in models. 19/ Knowing when to prune is just as key as knowing what to train. 20/ RAG isn’t a fine-tuning method. Use it before fine-tuning for best results. 21/ Fine-tuning without RAG is like writing without research. 22/ Combine RAG with LoRA to keep models fresh and informed. 23/ Use Hugging Face’s PEFT tools to skip the setup mess. 24/ LoraConfig and BitsAndBytesConfig make fine-tuning plug-and-play. 25/ Templates are available, don’t start from scratch. 26/ Don’t fine-tune everything. Target what changes, not what works. 27/ Avoid full-model updates unless absolutely needed. 28/ Aligning models is as much art as it is science. 29/ If you’re lost, follow the seven-stage pipeline in the guide. 30/ This paper is your north star if you care about efficient AI. Share this if you’ve ever asked, “Where’s the actual fine-tuning playbook?”

𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗟𝗟𝗠 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Large language models have transformed from simple text generators into intelligent reasoning systems powering search engines, enterprise copilots, and autonomous agents. Yet their accuracy, relevance, and efficiency depend on how we optimize them. There are three core techniques shaping this next wave of AI innovation: Context Engineering, Prompt Engineering, and Fine-Tuning. Each plays a distinct role, and the future belongs to those who know how to combine them effectively. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗚𝗼𝗮𝗹: Dynamically feed the model the right information at the right time without retraining. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Chunk and embed documents, store them in vector databases such as Pinecone, Weaviate, FAISS, or Milvus, and retrieve the most relevant content using retrieval augmented generation. Tools like LangChain and LlamaIndex orchestrate this process, ensuring token efficiency and building dynamic contexts. 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲: Enterprise knowledge assistants that instantly retrieve policies, Jira tickets, or AWS configurations on demand. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗚𝗼𝗮𝗹: Design high-quality prompts that maximize clarity, control, and reasoning depth. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Define objectives, structure zero-shot or few-shot examples, leverage chain-of-thought reasoning, and continuously refine outputs through iterative testing and feedback loops. Tools such as OpenAI Playground, LangSmith, PromptFlow, and Weights & Biases make experimentation and evaluation seamless. 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲: AI compliance reporting agents where precision and regulatory alignment are critical. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 𝗚𝗼𝗮𝗹: Permanently teach an LLM domain-specific knowledge or custom behavior. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Prepare high-quality labeled datasets, initialize a base model, and train using OpenAI Fine-Tuning API, Hugging Face Transformers, LoRA adapters, or AWS Sagemaker. Fine-tuning improves consistency and enables models to learn proprietary information and unique writing styles. 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲: Training a medical AI assistant with proprietary datasets to improve diagnostic accuracy and decision support. 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗟𝗟𝗠 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Prompt engineering guides behavior. Context engineering supplies knowledge. Fine-tuning builds expertise. When combined, these disciplines enable engineers to design scalable, explainable, and production ready AI systems. Follow Umair Ahmad for more insights. #AI #LLM #ContextEngineering #PromptEngineering #FineTuning #MachineLearning #SystemDesign

Day 10/30 of LLMs/SLMs - Pre-training, Fine-Tuning and PEFT Pretraining is where it all begins. The model learns language itself i.e. syntax, semantics, reasoning, by predicting the next word across vast amounts of data - books, code, Wikipedia, and the web. This phase is heavy: hundreds of billions of tokens, thousands of GPUs, weeks of training.  Models like GPT, LLaMA, and Mistral all start this way. Fine-tuning is where the model gets focused. You take that pretrained generalist and train it on your data - medical records, product descriptions, customer chats - so it speaks your domain’s language. Now the problem? Full fine-tuning can be computationally expensive. Especially when models have billions of parameters. Also updating all of the parameters for every task is wasteful. Enter PEFT. Instead of retraining the entire model, PEFT techniques tweak only a small subset of parameters, like <1%, while keeping the rest frozen. Three popular methods are - 👉 LoRA (Low-Rank Adaptation): Adds tiny trainable matrices to attention layers. It’s like giving your model small adjustment knobs rather than rewiring the whole circuit. Hugely popular and performant compared to the rest for domain adaptation (e.g., making a general LLM fluent in healthcare or finance jargon). 👉 Adapters: Small bottleneck layers inserted between transformer blocks. The are quick to switch out and this makes it possible to maintain multiple domain-specialized variants efficiently. 👉 Prefix-Tuning: Prepends learned “soft prompts” to the model’s inputs, teaching it how to behave differently without changing any internal weights. It’s also lightweight and ideal for few-shot or multitask scenarios. Takeaway ✅ Pretraining helps LLM builds general intelligence and Fine-tuning makes the model useful for your task. ✅ PEFT makes it efficient to fine-tune LLMs. LoRA for performance and adapters/prefix-finetuning for lightweight multi-task settings. Tune in tomorrow for more SLM/LLMs deep dives. -- 🚶➡️ To learn more about LLMs/SLMs, follow me - Karun! ♻️ Share so others can learn, and you can build your LinkedIn presence! (Img Src: https://lnkd.in/gUEG4_nM)

🚀 I am excited to share my journey into the world of Large Language Models (LLMs) and the game-changing concept of Parameter Efficient Fine-Tuning (PEFT)! Today, let's delve into why PEFT, specifically Low Rank Adaptation of Fine Tuning (LoRA), is a game-changer in maximizing efficiency without compromising quality. In my exploration, I've utilized the powerful FLAN-T5 model on AWS Sagemaker, working with the Dialogsum dataset. Initially, I evaluated the model's performance, and the results were. rouge1:0.23341586 rouge2:0.07603964 rougeL:0.20145521 rougeLsum:0.20145899 Then came the pivotal moment: Full fine-tuning. While this approach often yields impressive results, it demands significant computational resources. The ROUGE score for Full fine-tuning model are: rouge1:0.410266 rouge2:0.178406 rougeL:0.297702 rougeLsum:0.298737 But why go the traditional route when innovation offers us a more efficient path? Enter LoRA. By strategically reducing the number of trainable parameters, LoRA slashes resource requirements while maintaining performance quality. How? By freezing weights and injecting a pair of rank decomposition matrices alongside original weights. These lower-rank matrices are trained and seamlessly integrated into the model. The results? Astounding. Not only did the LoRA fine-tuned model achieve similar performance quality, but it did so with a mere 1.4% of the original parameter count! Talk about efficiency at its finest. rouge1:0.37253511 rouge2:0.12138812 rougeL:0.2762064 rougeLsum:0.27581349 So, what does this mean for the future of LLMs? It signifies a shift towards smarter, more resource-conscious fine-tuning methodologies. With LoRA paving the way, we're unlocking the potential to achieve exceptional results while saving invaluable compute resources. Join me in embracing the power of PEFT and revolutionizing the landscape of large language models—one efficient fine-tune at a time. #machinelearning #llm #nlp #AI #finetuning #PEFT #LoRA #FLANT5 #Dialogsum #aws #sagemaker 🌟🔍  

ATLAS: approach to finetune LLM Agents by identifying critical steps in expert trajectories Current LLM agent tuning approaches typically employ supervised finetuning on entire expert trajectories, which introduces expert bias and weaken generalization to states not covered by the expert data. To address it, this paper proposes ATLAS(Agent Tuning via Learning Critical Steps) that identifies the critical steps in expert trajectories and finetunes LLMs solely on these steps with reduced costs 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄 i) Using a oracle LLM(GPT4o by default) as selector, identify critical steps in expert trajectories collected in multiple environments ii) Construct a dataset of critical steps, which highlights the most impactful actions in the trajectories. iii) Training loss is only computed on the critical steps, with goal of optimize the LLMs by computing and minimizing the loss on these steps. 𝗠𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝘆 i) Definition of Critical Steps - Critical steps are key actions or states within an agent’s trajectory that have a substantial impact on the return, and are essential for ensuring the success of the trajectory ii) Selection of Critical Steps a) Plan Creation: Steps where the LLM agent formulates sub-goals by analyzing previous observations and considering the final objective b) Critical Observation: Steps where the LLM agent identifies and analyzes key information from the environment c) Critical Action: Steps where the LLM agent takes decisive and impactful actions based on prior observations, significantly advancing the process toward the final objectives. d) Self Correction: Steps where the LLM agent carefully recalls and assesses its previous actions or decisions iii) Agent Tuning on Critical Steps - Using the critical step dataset, optimize the model by calculating the loss exclusively on these critical steps 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀 i) Environments a) Held-in environments  - refer to the set of environments that are included in the training dataset. - environments in which the models can be fine-tuned, allowing them to learn and adapt their behavior based on the provided expert trajectories b) Held-out environments - consist of environments and tasks that are excluded from the training process - used to assess the model’s generalization capabilities, determining how effectively it can apply learned knowledge to novel and unseen scenarios.  ii) Results - Agents finetuned on 30% critical steps outperforms the baseline agents finetuned on 100% steps, especially in the multi-task learning scenario, mitigating the expert bias and negative transfer across tasks - ATLAS-finetuned agent achieves a better generalization capability than baseline agents on not only held-in but also held-out tasks 𝗕𝗹𝗼𝗴: https://lnkd.in/ezQZfKZS 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/e6rfPU_E 𝗣𝗿𝗼𝗺𝗽𝘁𝘀 𝘂𝘀𝗲𝗱: Page 16 of the paper mentioned above