Advanced LLM Parameter Tuning Techniques

If you’re building LLM applications today, reasoning is where the real leverage lies. And yet, I see a lot of engineers still treating LLM outputs as a single-shot black box. LLMs can reason, but only if you give them the right scaffolding and the right post-training. Here’s a mental model I’ve been using to think about LLM reasoning methods (see chart below): ✅ Inference-time reasoning methods: These are techniques that can be applied at inference time, without needing to retrain your model: → Tree of Thoughts (ToT), search through reasoning paths → Chain of Thought (CoT) prompting, prompt models to generate intermediate reasoning steps → Reasoning + Acting, use tools or function calls during reasoning → Self-feedback, prompt the model to critique and refine its own output → Episodic Memory Agents, maintain a memory buffer to improve multi-step reasoning → Self-consistency, sample multiple reasoning paths and select the most consistent answer ✅ Training-time enhancements: Where things get really powerful is when you post-train your model to improve reasoning, using human annotation or policy optimization: → Use Preference pairs and Reward Models to tune for better reasoning (RFT, Proximal PO, KL Regularization) → Apply RLHF, PPO + KL, Rejection Sampling + SFT, Advantage Estimation, and other advanced techniques to guide the model’s policy → Leverage multiple paths, offline trajectories, and expert demonstrations to expose the model to rich reasoning signals during training Here are my 2 cents 🫰 If you want production-grade LLM reasoning, you’ll need both, → Smart inference-time scaffolds to boost reasoning without slowing latency too much → Carefully tuned post-training loops to align the model’s policy with high-quality reasoning patterns → We’re also seeing increasing use of Direct Preference Optimization (DPO) and reference-free grading to further improve reasoning quality and stability. I’m seeing more and more teams combine both strategies, and the gap between "vanilla prompting" and "optimized reasoning loops" is only getting wider. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

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

Achieving 3x-25x Performance Gains for High-Quality, AI-Powered Data Analysis Asking complex data questions in plain English and getting precise answers feels like magic, but it’s technically challenging. One of my jobs is analyzing the health of numerous programs. To make that easier we are building an AI app with Sapient Slingshot that answers natural language queries by generating and executing code on project/program health data. The challenge is that this process needs to be both fast and reliable. We started with gemini-2.5-pro, but 50+ second response times and inconsistent results made it unsuitable for interactive use. Our goal: reduce latency without sacrificing accuracy. The New Bottleneck: Tuning "Think Time" Traditional optimization targets code execution, but in AI apps, the real bottleneck is LLM "think time", i.e. the delay in generating correct code on the fly. Here are some techniques we used to cut think time while maintaining output quality: ① Context-Rich Prompts Accuracy starts with context. We dynamically create prompts for each query: ➜ Pre-Processing Logic: We pre-generate any code that doesn't need "intelligence" so that LLM doesn't have to ➜ Dynamic Data-Awareness: Prompts include full schema, sample data, and value stats to give the model a full view. ➜ Domain Templates: We tailor prompts for specific ontology like "Client satisfaction" or "Cycle Time" or "Quality". This reduces errors and latency, improving codegen quality from the first try. ② Structured Code Generation Even with great context, LLMs can output messy code. We guide query structure explicitly: ➜ Simple queries: Direct the LLM to generate a single line chained pandas expression. ➜ Complex queries : Direct the LLM to generate two lines, one for processing, one for the final result Clear patterns ensure clean, reliable output. ③ Two-Tiered Caching for Speed Once accuracy was reliable, we tackled speed with intelligent caching: ➜ Tier 1: Helper Cache – 3x Faster ⊙ Find a semantically similar past query ⊙ Use a faster model (e.g. gemini-2.5-flash) ⊙ Include the past query and code as a one-shot prompt This cut response times from 50+s to <15s while maintaining accuracy. ➜ Tier 2: Lightning Cache – 25x Faster ⊙ Detect duplicates for exact or near matches ⊙ Reuse validated code ⊙ Execute instantly, skipping the LLM This brought response times to ~2 seconds for repeated queries. ④ Advanced Memory Architecture ➜ Graph Memory (Neo4j via Graphiti): Stores query history, code, and relationships for fast, structured retrieval. ➜ High-Quality Embeddings: We use BAAI/bge-large-en-v1.5 to match queries by true meaning. ➜ Conversational Context: Full session history is stored, so prompts reflect recent interactions, enabling seamless follow-ups. By combining rich context, structured code, caching, and smart memory, we can build AI systems that deliver natural language querying with the speed and reliability that we, as users, expect of it.

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

Good paper on A Deep Dive into Reasoning Large Language Models. Link in comments: Key Methodologies in Post-Training: 1) Fine-tuning: This involves further training pre-trained LLMs on smaller, carefully selected datasets to adapt them for particular tasks or domains. a) Instruction finetuning trains models on instruction-response pairs to improve their ability to follow user commands accurately and helpfully. b) Domain-specific finetuning specialises LLMs for expert areas such as biomedicine, finance, or law by using relevant text and labelled examples. This can involve tasks like classification, information retrieval, and question answering specific to the domain c) Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA, Prefix Tuning, and Adapters reduce computational costs by training only a small number of new parameters while keeping most of the original model parameters fixed. d) Distillation-based finetuning uses a more capable 'teacher' model to generate data or reasoning steps that a smaller 'student' model is then trained to reproduce, often resulting in more efficient models. 2) Reinforcement Learning (RL): RLHF plays a vital role in refining the reasoning, safety, and alignment of LLMs with human values. a) Reward modelling involves training a separate model to predict human preferences between different responses to a given prompt. Algorithms such as Proximal Policy Optimization (PPO) and Direct b) Preference Optimization (DPO) are used to optimize the LLM's behavior based on the learned reward model or preference data. Group Relative Policy Optimization (GRPO) is another technique that can improve training efficiency. 3) Test-Time Scaling: This refers to dynamically adjusting the computational resources used during the inference process to balance performance and cost. Challenges in Post-Training: Catastrophic forgetting: The tendency of LLMs to lose or degrade previously learned abilities when they are trained on new information. Reward hacking: The risk that models might learn to exploit the reward function in unintended ways that do not truly reflect the desired outcome. Inference-time trade-offs: The need to find a balance between achieving high performance and keeping the computational cost of deployment manageable. Emerging Directions: Model Alignment: Ensuring that LLMs are safe, ethical, and behave as intended by aligning their behavior with human values and expectations. Scalable Adaptation: Developing more efficient methods for adapting LLMs to new tasks and domains, especially when data and computational resources are limited. Inference-Time Reasoning: Enhancing the reasoning capabilities of LLMs during their deployment. Techniques like Chain-of-Thought, where models explicitly show their step-by-step reasoning, Tree of Thoughts (ToT), which explores multiple reasoning paths, and Graph of Thoughts (GoT), which uses more flexible graph-based structures for reasoning, are being actively researched.

Evolution strategies at scale: A new way to fine tune LLMs For a while now, reinforcement learning has been the standard way to fine tune LLMs. It works in the action space, i.e. exploring alternative actions to maximize reward, which is difficult especially with long action sequences. If we could explore LLM parameters directly, it might be possible to find more systematic and creative changes. Indeed parameter-space fine tuning is possible, and in a surprising way: evolution strategies, i.e. population-based search, can be scaled up to optimize billions of parameters in LLMs. It outperforms PPO and GRPO in a symbolic reasoning task, for example, while being more consistent and sample-efficient. Its gradient-free nature makes it a promising alternative in environments where RL is fragile or costly to apply. Read the full paper: https://lnkd.in/gGtyddty Read the blog (with a video and a conceptual illustration): https://lnkd.in/gZtWccrJ Explore the code: https://lnkd.in/gtJ95Nvb #AIResearch #EvolutionStrategies #LLM