AI Model Training Strategies

If you’re leading AI initiatives, here is a strategic cheat sheet to move from "𝗰𝗼𝗼𝗹 𝗱𝗲𝗺𝗼" to 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘃𝗮𝗹𝘂𝗲. Think Risk, ROI, and Scalability. This strategy moves you from "𝘄𝗲 𝗵𝗮𝘃𝗲 𝗮 𝗺𝗼𝗱𝗲𝗹" to "𝘄𝗲 𝗵𝗮𝘃𝗲 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝘀𝘀𝗲𝘁." 𝟭. 𝗧𝗵𝗲 "𝗪𝗵𝘆" 𝗚𝗮𝘁𝗲 (𝗣𝗿𝗲-𝗣𝗼𝗖) • Don’t build just because you can. Define the Business Problem first • Success: Is the potential value > 10x the estimated cost? • Decision: If the problem can be solved with Regex or SQL, kill the AI project now. 𝟮. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗼𝗳 𝗼𝗳 𝗖𝗼𝗻𝗰𝗲𝗽𝘁 (𝗣𝗼𝗖) • Goal: Prove feasibility, not scalability. • Timebox: 4–6 weeks max. • Team: 1-2 AI Engineers + 1 Domain Expert (Data Scientist alone is not enough). • Metric: Technical feasibility (e.g., "Can the model actually predict X with >80% accuracy on historical data?") 𝟯. 𝗧𝗵𝗲 "𝗠𝗩𝗣" 𝗧𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻 (𝗧𝗵𝗲 𝗩𝗮𝗹𝗹𝗲𝘆 𝗼𝗳 𝗗𝗲𝗮𝘁��) • Shift from "Notebook" to "System." • Infrastructure: Move off local GPUs to a dev cloud environment. Containerize. • Data Pipeline: Replace manual CSV dumps with automated data ingestion. • Decision: Does the model work on new, unseen data? If accuracy drops >10%, halt and investigate "Data Drift." 𝟰. 𝗥𝗶𝘀𝗸 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 (𝗧𝗵𝗲 "𝗟𝗮𝘄𝘆𝗲𝗿" 𝗣𝗵𝗮𝘀𝗲) • Compliance is not an afterthought. • Guardrails: Implement checks to prevent hallucination or toxic output (e.g., NeMo Guardrails, Guidance). • Risk Decision: What is the cost of a wrong answer? If high (e.g., medical advice), keep a "Human-in-the-Loop." 𝟱. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 • Scalability & Latency: Users won’t wait 10 seconds for a token. • Serving: Use optimized inference engines (vLLM, TGI, Triton) • Cost Control: Implement token limits and caching. "Pay-as-you-go" can bankrupt you overnight if an API loop goes rogue. 𝟲. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 • Automated Eval: Use "LLM-as-a-Judge" to score outputs against a golden dataset. • Feedback Loops: Build a mechanism for users to Thumbs Up/Down outcomes. Gold for fine-tuning later. 𝟳. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 (𝗟𝗟𝗠𝗢𝗽𝘀) • Day 2 is harder than Day 1. • Observability: Trace chains and monitor latency/cost per request (LangSmith, Arize). • Retraining: Models rot. Define when to retrain (e.g., "When accuracy drops below 85%" or "Monthly"). 𝗧𝗲𝗮𝗺 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 • PoC Phase: AI Engineer + Subject Matter Expert. • MVP Phase: + Data Engineer + Backend Engineer. • Production Phase: + MLOps Engineer + Product Manager + Legal/Compliance. 𝗛𝗼𝘄 𝘁𝗼 𝗺𝗮𝗻𝗮𝗴𝗲 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 (𝗺𝘆 𝗮𝗱𝘃𝗶𝗰𝗲): → Treat AI as a Product, not a Research Project. → Fail fast: A failed PoC cost $10k; a failed Production rollout costs $1M+. → Cost Modeling: Estimate inference costs at peak scale before you write a line of production code. What decision gates do you use in your AI roadmap? Follow Priyanka for more cloud and AI tips and tools #ai #aiforbusiness #aileadership

AI Learns Complex Math Reasoning on Its Own ... Exciting new research from Alibaba Group is pushing the boundaries of what's possible with AI for mathematical problem-solving. Their "AlphaMath Almost Zero" approach enables AI models to learn advanced mathematical reasoning with minimal human supervision. 👉 The Challenge of Teaching AI Complex Reasoning Training AI to solve multistep math problems has traditionally been very challenging. Consider this example: "A store sells a total of 550 cups and plates. If the store has 125 more cups than plates, how many plates does the store have?" To solve this, the AI needs to: 1. Understand the total number of items is 550  2. Recognize there are 125 more cups than plates 3. Set up an equation like: cups + plates = 550 and cups = plates + 125 4. Solve the system of equations to determine the number of plates Generating training data to teach AI each of these reasoning steps normally requires math experts to manually break down and annotate thousands of problems. 👉 Automating Training Data Generation AlphaMath Almost Zero takes a radically different approach. The researchers leverage: 1. A pre-trained language model that already has significant mathematical knowledge  2. Monte Carlo Tree Search (MCTS) - a technique to efficiently search the space of possible solution paths 3. Only the initial math question and final answer (no intermediate steps) MCTS is used to have the language model generate its own step-by-step solutions, both correct and incorrect. By strategically searching for both good and bad solutions using MCTS, the model can generate its own high-quality training data without needing manual annotation. This significantly reduces the cost and expertise needed to train AI for complex reasoning. 👉 Guiding Better Reasoning with Step-Level Value Models But how does the AI know if an intermediate reasoning step is on the right track? AlphaMath Almost Zero introduces a step-level value model to evaluate the quality of each step. As the language model generates a solution, the value model assesses how useful each step is for reaching the correct final answer. This guides the AI to spend more time exploring promising solution paths while avoiding dead-ends. Interestingly, this mimics how humans dynamically adapt their problem-solving based on how well they think it's going. 👉 Iterative Autonomous Improvement What's really remarkable is that AlphaMath Almost Zero can progressively improve its mathematical reasoning through an iterative training approach: 1. Start with a pre-trained language model and an untrained value model  2. Use MCTS to generate solution attempts 3. Train the language model and value model based on which solutions were successful 4. Repeat from step 2 with the improved models to generate even better solutions Through this cycle, the AI can get better and better at complex mathematical reasoning over time, with minimal human supervision.

I recently spent time getting more hands-on with LLM & Agentic AI engineering through Ed Donner's training. Instead of stopping at examples, I built a mini multi-agent logistics delivery optimization framework. Building real AI systems quickly makes one thing clear: 𝙏𝙝𝙚 𝙝𝙖𝙧𝙙 𝙥𝙖𝙧𝙩 𝙞𝙨𝙣’𝙩 𝙩𝙝𝙚 𝙢𝙤𝙙𝙚𝙡 — 𝙞𝙩’𝙨 𝙩𝙝𝙚 𝙖𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚 𝙙𝙚𝙘𝙞𝙨𝙞𝙤𝙣𝙨 𝙖𝙧𝙤𝙪𝙣𝙙 𝙞𝙩. A few practical lessons: 1. 𝗟𝗟𝗠 𝗺𝗼𝗱𝗲𝗹 𝘀𝗲𝗹𝗲𝗰𝘁��𝗼𝗻 𝗶𝘀 𝗳𝗮𝗿 𝗺𝗼𝗿𝗲 𝗻𝘂𝗮𝗻𝗰𝗲𝗱 𝘁𝗵𝗮𝗻 𝗰𝗼𝘀𝘁 𝘃𝘀 𝗹𝗮𝘁𝗲𝗻𝗰𝘆. Trade-offs: • reasoning maturity for complex planning • context window & memory strategy • proprietary models vs smaller open models • infra costs (GPU/hosting) vs token-based API costs • tool-calling reliability & structured output adherence • benchmark performance vs real task behavior • model stability across releases In practice, it becomes a hybrid strategy: 𝘀𝗺𝗮𝗹𝗹𝗲𝗿/𝗰𝗵𝗲𝗮𝗽𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿 𝗿𝗼𝘂𝘁𝗶𝗻𝗲 𝘁𝗮𝘀𝗸𝘀 + 𝗦𝗟𝗠 𝘄𝗶𝘁𝗵 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗱𝗼𝗺𝗮𝗶𝗻 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 + 𝘀𝘁𝗿𝗼𝗻𝗴𝗲𝗿 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀. 𝟮. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗮𝘀 𝗺𝘂𝗰𝗵 𝗮𝘀 𝘁𝗵𝗲 𝗟𝗟𝗠: Many AI demos over-engineer the stack. In reality, simplicity, latency, security and reliability matter more than novelty. • Use orchestration frameworks only where coordination complexity exists • Combine prompts with structured outputs to reduce ambiguity • Watch serialization and tool-call overhead — they impact latency and UX • Reduce unnecessary LLM calls when deterministic code can solve the task Besides lowering token cost, this improves context efficiency, letting models focus on real reasoning. Sometimes best architecture decision is 𝙣𝙤𝙩 𝙞𝙣𝙩𝙧𝙤𝙙𝙪𝙘𝙞𝙣𝙜 𝙖𝙣𝙤𝙩𝙝𝙚𝙧 𝙡𝙖𝙮𝙚𝙧. 3. 𝗕𝗶𝗴𝗴𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝘀 ≠ 𝗯𝗲𝘁𝘁𝗲𝗿 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀 Smaller models with fine-tuning on domain data can perform more consistently than larger ones. Fine-tuning helps when: • tasks are repetitive but require precision • domain vocabulary is specialized • prompts become fragile But 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝗮𝗹𝘀𝗼 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗲𝘀 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗼𝘃𝗲𝗿𝗵𝗲𝗮𝗱. Base model upgrades trigger retesting and partial rewrites. 4. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗴𝗮𝗽: 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 → 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 Demos are easy. Production requires 𝙚𝙫𝙖𝙡𝙪𝙖𝙩𝙞𝙤𝙣 𝙛𝙧𝙖𝙢𝙚𝙬𝙤𝙧𝙠𝙨, 𝙤𝙗𝙨𝙚𝙧𝙫𝙖𝙗𝙞𝙡𝙞𝙩𝙮, 𝙨𝙚𝙘𝙪𝙧𝙞𝙩𝙮, 𝙥𝙚𝙧𝙛𝙤𝙧𝙢𝙖𝙣𝙘𝙚, 𝙘𝙤𝙨𝙩 𝙜𝙤𝙫𝙚𝙧𝙣𝙖𝙣𝙘𝙚 & 𝙜𝙪𝙖𝙧𝙙𝙧𝙖𝙞𝙡𝙨. That’s where most engineering effort goes. 𝟱. 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗔𝗜 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝘀 Many AI conversations focus on SDLC productivity- Useful but the bigger opportunity is 𝙧𝙚𝙞𝙢𝙖𝙜𝙞𝙣𝙞𝙣𝙜 𝙡𝙚𝙜𝙖𝙘𝙮 𝙗𝙪𝙨 𝙥𝙧𝙤𝙘𝙚𝙨𝙨𝙚𝙨 𝙪𝙨𝙞𝙣𝙜 𝘼𝙜𝙚𝙣𝙩𝙞𝙘 AI. By simply automating existing steps, we risk making inefficient tasks efficient and missing the real transformation.

This is what you need to understand the latest AI models: This survey breaks down exactly how modern LLMs like GPT-4, Claude, and Llama 3 develop their impressive reasoning abilities. The paper provides a systematic exploration of what happens AFTER the initial pre-training of LLMs, breaking down three critical post-training approaches: 1. Fine-tuning: How models are adapted to specific domains and tasks 2. Reinforcement Learning: How models are aligned with human preferences and values 3.Test-time Scaling: How inference-time techniques enhance reasoning without changing model weights What makes this paper particularly valuable is how it connects these techniques to real-world models like GPT-4, Claude, Llama 3, and DeepSeek-R1, showing exactly which post-training methods contribute to their capabilities. The authors also maintain a continuously updated repository tracking developments in this fast-moving field: https://lnkd.in/dyE-BWmR For anyone trying to understand why some LLMs reason better than others, or how the field is evolving beyond simply scaling up model size, this paper offers invaluable insights into the techniques that are shaping the next generation of AI systems. Read the full paper here: https://lnkd.in/dUgU8x3R #ArtificialIntelligence #LLM #MachineLearning #AI #DeepLearning #NLP #ReinforcementLearning

Training reliable tool-using agents is notoriously difficult. It often presents a trade-off: rely on expensive manual human intervention or settle for "simulated" environments where an LLM judges another LLM (often unverifiable). A new paper, "ASTRA" (Automated Synthesis of agentic Trajectories and Reinforcement Arenas), proposes a fully automated solution to close this gap. 🤖 Here is the breakdown of how it works: 1. Verifiable Environments over Simulation Instead of relying on LLM-based simulators for feedback, ASTRA synthesizes executable environments. It converts Question-Answer traces into independent, code-executable Python environments. This allows the Reinforcement Learning (RL) process to receive deterministic, rule-based rewards rather than "vibes-based" feedback. 2. Two-Stage Training Pipeline The framework utilizes a complementary approach: -  SFT (Supervised Fine-Tuning): Uses synthesized trajectories based on tool-call graphs to give the model a strong "cold start" in tool usage. -  Online Multi-Turn RL: The agent interacts with the synthesized environments. Crucially, the training mixes in "irrelevant tools" (distractors). This forces the agent to learn tool discrimination rather than just memorizing which tool to pick. 3. Performance The results are significant for the open-source community. On agentic benchmarks like BFCL v3 and ACEBench, ASTRA-trained models (14B and 32B) achieve state-of-the-art performance for their size, approaching the capabilities of closed-source systems while preserving their core reasoning abilities. Limitations: While the automated environment synthesis is scalable, it is computationally expensive to generate these verifiable sandboxes. Additionally, the current framework focuses on goal-oriented tasks and has not yet fully integrated complex, multi-turn human-user interactions during training. The full pipeline and models have been open-sourced. 🛠️ #MachineLearning #AI #LLM #ToolCalling #AgenticAI

How New AI Models Are Actually Built  (A Simple Breakdown for Business Leaders & Everyday Users) AI is everywhere right now but very few people understand how an AI model is truly created from scratch. It’s not one step. It’s not “just train a model.” It’s a full end-to-end process that blends strategy, data, experimentation, and engineering. Here’s the easy, business-friendly explanation 👇 1️⃣ Set the Objectives Every great AI model starts with clarity, not code. • What problem are we solving? • Is AI even the right solution? • What does success look like? AI strategy matters more than AI hype. 2️⃣ Prepare the Data This is the most time-consuming step, and often the most important. • Collect the data • Clean the data • Engineer features • Split into training and testing sets If the data is weak, the AI will be weak. No algorithm can save bad data. 3️⃣ Choose the Algorithm Now you decide how the model will learn. • Regression? • Neural networks? • Decision trees? • Something else? And you pick the tech stack: TensorFlow, PyTorch, scikit-learn, etc. The right algorithm depends on the problem and the constraints. 4️⃣ Train the Model This is where learning happens: • Feed the model examples • Adjust its internal settings • Optimize performance • Iterate, iterate, iterate No model gets it right the first time. Training is nonstop refinement. 5️⃣ Evaluate & Test Before deploying, you stress-test the model: • Accuracy • Errors • Reliability • Bias checks • Real-world performance If it only works in the lab, it doesn’t work. 6️⃣ Deploy the Model This is the final step, turning a prototype into a real product. • Choose a deployment strategy • Build an API • Containerize the model • Monitor performance in production This is how AI moves from “cool demo” to business value. Why this matters AI isn’t magic. It’s structured problem-solving. When leaders understand these steps, they can: • Ask the right questions • Spot unrealistic AI proposals • Make smarter investment decisions • Build solutions that actually create value The companies winning with AI are the ones that understand the process not just the buzzwords. 🔁 Repost to help more people understand how AI models are really built. ➕ Follow Gabriel Millien for clear, practical breakdowns on LLMs, AI systems, and the future of intelligent tools. CC: ByteByteGo

I'm kicking off a series of posts here that I hope will gain some traction. Post 1: How to teach an LLM hard things without it forgetting how to tie its shoes Over the past 3 weeks, AI21 Labs released several posts under "Labs In Front"—a fascinating look into advanced but intuitive details about LLM training from some of the best AI researchers in Israel. Some background: A while back, DeepSeek introduced GRPO—a method for fine-tuning LLMs by having them generate multiple answers and automatically scoring which are correct. No human feedback needed. It works especially well for verifiable tasks (math, coding), but surprisingly improves models across the board. It's now industry standard. The problem: As training progresses, most tasks become too easy—the model outputs 10 answers, all correct, learns nothing. Or tasks are impossible (like RAG questions with no answer in the documents). Either way: zero learning signal. The researchers found that up to 85% of training outputs become useless. The naive fix: Stop showing examples the model already gets right. But this causes "starvation"—the model sees only hard examples and starts forgetting the easy stuff. Training becomes unstable. Even worse when learning multiple tasks: it keeps grinding on hard problems while forgetting how to tie its shoes. The solution: Brilliantly simple. Set an "alarm clock." If the model aces an example, snooze it for a while—but periodically wake it up and ask: "Hey, you still remember how to tie shoes? No? Let's refresh." This lets the model tackle increasingly difficult problems without catastrophic forgetting. Result: 3X training efficiency—which translates to serious cost savings. Link to the full post in comments Daniel Gissin, Yuval Globerson Inbal Magar Yaniv Markovski Sharon Argov

Deep learning architectures have been getting larger every year, especially with the paradigm shift to foundation models. But are larger models aways better? Baifeng Shi et al. explored new training strategies to aid smaller models in achieving similar levels of accuracy to larger ones. They proposed Scaling on Scales (S²), where a smaller vision model is applied to multiple image scales, and evaluated its performance on multiple tasks. They used ViT-B or ViT-L for the S² models and compared with larger ViT-H and ViT-G models. In many cases, S² was able to match the accuracy of larger models. Larger models were shown to generalize better on hard examples, but this difference can be made up by teaching a smaller model with S² to approximate the features of the larger model with a projector layer. This new technique can enable us to use smaller models with reduced GPU costs while still achieving the high accuracy of larger models. https://lnkd.in/eYjFBPvq #MachineLearning #DeepLearning #ComputerVision __________________ Enjoyed this post? Like 👍, comment 💬, or re-post 🔄 to share with others. Click "View my newsletter" under my name ⬆️ to join 1500+ readers.

Most people think AI is just ChatGPT: The truth? 90% of the work happens before the response: It’s not the model that’s magic. It’s the data prep, the problem framing, the relentless iteration. Here’s how real AI is built (step by step): 1/ Problem Definition: ↳ Clearly define objectives, goals, and desired outcomes. ↳ Understand users' needs and possible challenges. 2/ Data Collection: ↳ Collect relevant, high-quality data sources. ↳ Prioritize accuracy and reliability. ↳ Minimize bias in the data. 3/ Data Preparation: ↳ Cleanse data and data sets. ↳ Structure data for easy analysis. 4/ Algorithm Selection: ↳ Evaluate available models (regression, clustering, etc.). ↳ Select algorithm based on task complexity. ↳ Ensure models are explainable. 5/ Model Training: ↳ Feed prepared data into selected algorithms. ↳ Adjust model parameters through optimization. ↳ Train until you receive satisfactorily accurate results. 6/ Testing and Validation: ↳ Assess model accuracy based on validation sets. ↳ Cross-validate to avoid over-fitting. ↳ Verify results for robustness and reliability. 7/ Iteration and Optimization: ↳ Refine models by tuning hyperparameters. ↳ Enhance data quality and retrain. ↳ Continuously optimize for best performance. 8/ Deployment: ↳ Integrate the trained model into production environments. ↳ Monitor real-time stability and performance. ↳ Ensure scalability and security of the deployment. 9/ Feedback and Monitoring: ↳ Collect continuous feedback from users and systems. ↳ Analyze performance metrics regularly. ↳ Update and recalibrate the model accordingly. 10/ Continuous Learning: ↳ Adapt models to handle new and evolving data. ↳ Regularly update models to reflect changing requirements. ↳ Maintain model relevancy and accuracy over time. Which step do you spend the most time on? Which step surprised you? Share below. Want to be more tech savvy? Follow me, Ashley Nicholson 🔔 . P.S. Like this? Share with your network. ♻️

The bottleneck isn't GPUs or architecture. It's your dataset. Three ways to customize an LLM: 1. Fine-tuning: Teaches behavior. 1K-10K examples. Shows how to respond. Cheapest option. 2. Continued pretraining: Adds knowledge. Large unlabeled corpus. Extends what model knows. Medium cost. 3. Training from scratch: Full control. Trillions of tokens. Only for national AI projects. Rarely necessary. Most companies only need fine-tuning. How to collect quality data: For fine-tuning, start small. Support tickets with PII removed. Internal Q&A logs. Public instruction datasets. For continued pretraining, go big. Domain archives. Technical standards. Mix 70% domain, 30% general text. The 5-step data pipeline: 1. Normalize. Convert everything to UTF-8 plain text. Remove markup and headers. 2. Filter. Drop short fragments. Remove repeated templates. Redact PII. 3. Deduplicate. Hash for identical content. Find near-duplicates. Do before splitting datasets. 4. Tag with metadata. Language, domain, source. Makes dataset searchable. 5. Validate quality. Check perplexity. Track metrics. Run small pilot first. When your dataset is ready: All sources documented. PII removed. Stats match targets. Splits balanced. Pilot converges cleanly. If any fail, fix data first. What good data does: Models converge faster. Hallucinate less. Cost less to serve. The reality: Building LLMs is a data problem. Not a training problem. Most teams spend 80% of time on data. That's the actual work. Your data is your differentiator. Not your model architecture. Found this helpful? Follow Arturo Ferreira.