Training Cycle Optimization

Your learning programs are failing for the same reason most people quit the gym. If your carefully designed learning program has the same completion rate as a January gym membership, you're making the same mistake as every mediocre fitness trainer. You're designing for an "average learner" who doesn't exist. Here's how smart learning designers can apply fitness training principles to create more impactful experiences: 1️⃣ Progressive Overload 🏋️♀️ In fitness: Gradually increasing weight, frequency, or reps to build strength and endurance. 🧠 In learning: Systematically increasing cognitive challenge to build deeper understanding. How to integrate in your next design: - Create tiered challenge levels within each learning module - Build knowledge checks that adapt difficulty based on previous performance - Include optional "challenge" activities for advanced learners - Document the progression pathway so learners can see their growth 2️⃣ Scaled Workouts 🏋️♀️ In fitness: Modifying exercises to match individual fitness levels while preserving movement patterns. 🧠 In learning: Adapting content complexity while maintaining core learning objectives. How to integrate in your next design: - Create three versions of each activity (beginner, intermediate, advanced) - Include prerequisite self-assessments that guide learners to appropriate starting points - Design scaffolded resources that can be added or removed based on learner needs - Allow multiple paths to demonstrate competency 3️⃣ Active Recovery 🏋️♀️ In fitness: Low-intensity activity between intense workouts that promotes healing and prevents burnout. 🧠 In learning: Structured reflection periods that consolidate knowledge and prevent cognitive overload. How to integrate in your next design: - Schedule reflection activities between challenging content sections - Create templates that prompt learners to connect new concepts to existing knowledge - Include peer teaching opportunities as a form of active learning recovery - Design "cognitive cooldowns" that close each module with key takeaway exercises 4️⃣ Periodisation 🏋️♀️ In fitness: Organising training into structured cycles with varying intensity and focus. 🧠 In learning: Cycling between concept acquisition, application, and mastery phases. How to integrate in your next design: - Map your curriculum into distinct learning phases (foundation, application, mastery) - Create "micro-cycles" within modules that alternate between content delivery and practice - Design culminating challenges at the end of each learning cycle - Include assessment "de-load" weeks with lighter workload but higher reflection The best learning experience isn't the one with the most content or the fanciest technology—it's the one designed for consistent progress through appropriate challenge. What fitness training principle will you incorporate in your next learning design?

🚀 𝐃𝐄𝐄𝐏 𝐋𝐄𝐀𝐑𝐍𝐈𝐍𝐆 𝐎𝐏𝐓𝐈𝐌𝐈Z𝐄𝐑𝐒 Training a neural network is an optimization problem: we are trying to find the "valley" in a massive, complex landscape of error. While the architecture defines the landscape, the 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐫 defines how we navigate it. Choosing the right one determines whether your model converges quickly, gets stuck in a local minimum, or explodes. 𝟏. 𝐓𝐇𝐄 𝐁𝐀𝐒𝐄𝐋𝐈𝐍𝐄𝐒: 𝐒𝐆𝐃 & 𝐌𝐎𝐌𝐄𝐍𝐓𝐔𝐌 𝐒𝐆𝐃 (𝐒𝐭𝐨𝐜𝐡𝐚𝐬𝐭𝐢𝐜 𝐆𝐫𝐚𝐝𝐢𝐞𝐧𝐭 𝐃𝐞𝐬𝐜𝐞𝐧𝐭): The simplest approach. it updates weights based on a small batch of data. While reliable, it can be slow and oscillate wildly in steep ravines. 𝐌𝐨𝐦𝐞𝐧𝐭𝐮𝐦: Adds a "velocity" term to SGD. Like a ball rolling down a hill, it gains speed in directions that consistently lead downward, helping it damp oscillations and blow past small "bumps" in the landscape. 𝟐. 𝐀𝐃𝐀𝐏𝐓𝐈𝐕𝐄 𝐋𝐄𝐀𝐑𝐍𝐈𝐍𝐆 𝐑𝐀𝐓𝐄𝐒: 𝐀𝐝𝐚𝐆𝐫𝐚𝐝 & 𝐑𝐌𝐒𝐩𝐫𝐨𝐩 Traditional optimizers use a single learning rate for all parameters. Adaptive optimizers change the rate for each individual weight. 𝐀𝐝𝐚𝐆𝐫𝐚𝐝: Gives frequently occurring features a small learning rate and rare features a large one. It is excellent for 𝐬𝐩𝐚𝐫𝐬𝐞 𝐝𝐚𝐭𝐚 (like NLP embeddings) but can stall if the learning rate decays too much. 𝐑𝐌𝐒𝐩𝐫𝐨𝐩: Solves AdaGrad's decay problem by using a moving average of squared gradients. It is the go-to for 𝐑𝐍𝐍𝐬 and non-stationary problems. 𝟑. 𝐓𝐇𝐄 𝐆𝐎𝐋𝐃 𝐒𝐓𝐀𝐍𝐃𝐀𝐑𝐃: 𝐀𝐝𝐚𝐦 & 𝐍𝐚𝐝𝐚𝐦 𝐀𝐝𝐚𝐦 (𝐀𝐝𝐚𝐩𝐭𝐢𝐯𝐞 𝐌𝐨𝐦𝐞𝐧𝐭 𝐄𝐬𝐭𝐢𝐦𝐚𝐭𝐢𝐨𝐧): The most popular optimizer in modern AI. It combines the benefits of both 𝐌𝐨𝐦𝐞𝐧𝐭𝐮𝐦 and 𝐑𝐌𝐒𝐩𝐫𝐨𝐩. It maintains a running average of both the gradients and their squares. 𝐍𝐚𝐝𝐚𝐦: Adds Nesterov Accelerated Momentum to Adam. This allows the model to "look ahead" before making a step, often leading to even faster and smoother convergence. 𝟒. 𝐒𝐂𝐀𝐋𝐄 & 𝐒𝐓𝐀𝐁𝐈𝐋𝐈𝐓𝐘: 𝐋𝐀𝐌𝐁 & 𝐀𝐝𝐚𝐃𝐞𝐥𝐭𝐚 𝐋𝐀𝐌𝐁 (𝐋𝐚𝐲𝐞𝐫-𝐰𝐢𝐬𝐞 𝐀𝐝𝐚𝐩𝐭𝐢𝐯𝐞 𝐌𝐨𝐦𝐞𝐧𝐭𝐬 𝐛𝐚𝐬𝐞𝐝 𝐟𝐨𝐫 𝐁𝐚𝐭𝐜𝐡): Specifically designed for training massive models like 𝐋𝐋𝐌𝐬 and Transformers. It allows for very large batch sizes without the training becoming unstable. 𝐀𝐝𝐚𝐃𝐞𝐥𝐭𝐚: A more robust version of AdaGrad that restricts the window of accumulated past gradients. Its primary advantage is that you don't need to manually set a starting learning rate. 💡 𝐒𝐓𝐑𝐀𝐓𝐄𝐆𝐈𝐂 𝐒𝐄𝐋𝐄𝐂𝐓𝐈𝐎𝐍 𝐆𝐔𝐈𝐃𝐄 𝐒𝐭𝐚𝐫𝐭 𝐇𝐞𝐫𝐞: Use 𝐀𝐝𝐚𝐦. It works well on almost everything with minimal tuning. 𝐂𝐨𝐦𝐩𝐥𝐞𝐱 𝐒𝐞𝐪𝐮𝐞𝐧𝐜𝐞𝐬: If training RNNs or LSTMs, try 𝐑𝐌𝐒𝐩𝐫𝐨𝐩. 𝐒𝐩𝐚𝐫𝐬𝐞 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬: If working with high-dimensional embeddings or NLP, 𝐀𝐝𝐚𝐆𝐫𝐚𝐝 is your best bet. 𝐆𝐢𝐚𝐧𝐭 𝐌𝐨𝐝𝐞𝐥𝐬: If you are scaling to huge clusters and massive batches, move to 𝐋𝐀𝐌𝐁.

Many people believe live trainings work better simply because people can talk to each other face‑to‑face, but that’s not the real reason. In reality, their effectiveness comes from something else entirely, they naturally follow a powerful learning rhythm. Great offline trainings follow one simple logic: action → reflection → understanding → application. This is Kolb’s Cycle. And it’s incredibly powerful. The problem? It was almost impossible to implement it in online learning. That’s why 90% of online courses look like “interactive lectures”: nice slides, videos, quizzes. But that’s content consumption, not transformation. And now - the unexpected twist. For the first time, online learning has caught up with offline experiences. Because AI removed the main barrier: it finally allows learners to get experience, reflection, and practice in a personalized way. Here’s how Kolb’s Cycle looks in modern learning design: 1️⃣ Concrete Experience — action Essence: the learner must do something, live through a situation, face a task — ideally experiencing difficulty or making a mistake that shows their current model doesn’t work. How online: role-based dialogue, scenario simulation. 2️⃣ Reflective Observation — reflection Essence: pause and think — what happened, what actions were taken, and why the result turned out this way. How online: interactive reflection prompts; AI coach provides feedback based on performance and the learner’s own reflections. 3️⃣ Abstract Conceptualisation — understanding Essence: form a new behavioural model — concepts, principles, algorithms that explain how to act more effectively. How online: short video lecture, model breakdown, interactive frameworks, checklists, interactive infographics. 4️⃣ Active Experimentation — application Essence: try the new model in a safe environment and observe the result. How online: AI-based simulation, situational exercise, case-solving with the new approach; AI coach supports and adjusts. The outcome? Online learning stops being “content” and becomes a behaviour tracker. A course becomes a training simulator, not a film. Kolb’s Cycle finally becomes real in digital learning. Do you use this framework? What results have you seen?

Training a Large Language Model (LLM) involves more than just scaling up data and compute. It requires a disciplined approach across multiple layers of the ML lifecycle to ensure performance, efficiency, safety, and adaptability. This visual framework outlines eight critical pillars necessary for successful LLM training, each with a defined workflow to guide implementation: 𝟭. 𝗛𝗶𝗴𝗵-𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗗𝗮𝘁𝗮 𝗖𝘂𝗿𝗮𝘁𝗶𝗼𝗻: Use diverse, clean, and domain-relevant datasets. Deduplicate, normalize, filter low-quality samples, and tokenize effectively before formatting for training. 𝟮. 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Design efficient preprocessing pipelines—tokenization consistency, padding, caching, and batch streaming to GPU must be optimized for scale. 𝟯. 𝗠𝗼𝗱𝗲𝗹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗗𝗲𝘀𝗶𝗴𝗻: Select architectures based on task requirements. Configure embeddings, attention heads, and regularization, and then conduct mock tests to validate the architectural choices. 𝟰. 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 and 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Ensure convergence using techniques such as FP16 precision, gradient clipping, batch size tuning, and adaptive learning rate scheduling. Loss monitoring and checkpointing are crucial for long-running processes. 𝟱. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 & 𝗠𝗲𝗺𝗼𝗿𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Leverage distributed training, efficient attention mechanisms, and pipeline parallelism. Profile usage, compress checkpoints, and enable auto-resume for robustness. 𝟲. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 & 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: Regularly evaluate using defined metrics and baseline comparisons. Test with few-shot prompts, review model outputs, and track performance metrics to prevent drift and overfitting. 𝟳. 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗦𝗮𝗳𝗲𝘁𝘆 𝗖𝗵𝗲𝗰𝗸𝘀: Mitigate model risks by applying adversarial testing, output filtering, decoding constraints, and incorporating user feedback. Audit results to ensure responsible outputs. 🔸 𝟴. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 & 𝗗𝗼𝗺𝗮𝗶𝗻 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Adapt models for specific domains using techniques like LoRA/PEFT and controlled learning rates. Monitor overfitting, evaluate continuously, and deploy with confidence. These principles form a unified blueprint for building robust, efficient, and production-ready LLMs—whether training from scratch or adapting pre-trained models.

If you’re building LLMs for reasoning or agentic behavior - understanding how to train them with reinforcement learning is becoming an essential skill. After pre-training, most LLMs go through post-training to align with human preferences - this is where RLHF (Reinforcement Learning with Human Feedback) comes in. It helps models become: → more helpful → less toxic → better at following instructions → more aligned to business goals But the field is moving beyond simple human feedback toward Reinforcement Learning with Verifiable Rewards: → structured, reliable reward signals → improved reasoning and multi-step behavior → more factual and controllable outputs Here’s how it works - and why methods like PPO, GRPO, and DPO matter. ✅ PPO (Proximal Policy Optimization) → The classic RLHF loop used widely today. → You collect preference labels → train a Reward Model → fine-tune the LLM with PPO. → PPO allows stable updates by constraining large policy shifts. → KL regularization ensures the model stays close to the base. Cycle: Policy → Output → Reward Model → Update → Repeat. ✅ GRPO (Group-based Reinforcement Policy Optimization) → A newer approach focused on group-level optimization. → You optimize over groups of outputs, not just individual samples. → Rewards and KL regularization are computed batch-wise → enabling more stable and scalable RLHF. → Useful when optimizing for complex reasoning and verifiable tasks. Example: teaching an LLM to follow logical proofs or multi-step reasoning chains accurately. ✅ DPO (Direct Preference Optimization) → The simplest and fastest method. → No separate reward model needed. → You directly optimize the policy to prefer outputs ranked better by humans. → DPO compares likelihood of preferred vs. rejected outputs and adjusts the model. Ideal when: → You have good preference data. → You want a lightweight, scalable fine-tuning method. → You don’t want full RL infra. 𝗦𝗼 𝗶𝗻 𝗮 𝗻𝘂𝘁𝘀𝗵𝗲𝗹𝗹: → PPO - classic RLHF with Reward Model + PPO optimizer. → GRPO - group-level optimization with verifiable rewards. → DPO - direct preference-based optimization, simple and fast. 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿❓ LLMs are moving from simple chatbots toward: → deeper reasoning → multi-step agents → long-context understanding → real-world tool use To get there, we need alignment with more verifiable reward signals - not just polite answers, but grounded, reliable, and accurate behavior. Methods like PPO, GRPO, and DPO are key tools in the evolving LLM training stack. ------ Share this with your network to spread the knowledge ♻️ Follow me (Aishwarya Srinivasan) for more AI educational content and insights to keep you up-to date about the AI/ML field.

Training massive LLMs used to be painfully slow, but not anymore! DeepSpeed is an open-source deep learning optimization library that makes it easy to train and fine-tune models that would otherwise be too large, too slow, or too expensive. It powers many state-of-the-art LLMs and brings the kind of system-level optimizations needed for massive training runs. ZeRO (Zero Redundancy Optimizer) is the core of DeepSpeed. It partitions optimizer states, gradients, and parameters across GPUs which removes memory duplication and lets you train models far beyond single GPU limits. Here’s why DeepSpeed matters: • ZeRO-powered memory optimization for training trillion-parameter scale models • Up to 10x faster training through kernel optimizations and parallelism techniques • 3D parallelism combining data, tensor, and pipeline parallel training • Inference optimizations that reduce latency and memory use for serving large LLMs • Works with PyTorch and slots directly into existing training loops DeepSpeed handles the engineering complexity behind distributed training so you can run larger models, bigger batches, and faster iterations without restructuring your codebase. It is 100% Open Source Link to the Github Repo in the comments!

Sufficient is better than optimal for training neural networks Optimization is the default mindset in deep learning: pick an architecture, run Adam or SGD until the loss is as small as possible, then hope regularization keeps overfitting under control. But what if the problem isn’t the models or the data—but the obsession with optimal training itself? Irina Babayan and coauthors propose a different view: instead of chasing a single best minimum, train networks to be merely “good enough” and deliberately stay near, not at, the optimum. Their method, simmering, borrows tools from statistical physics. Network weights are treated like particles moving in a loss landscape at a small but finite temperature. A Nosé–Hoover thermostat keeps this system from settling into a sharp minimum, continually exploring a cloud of near-optimal solutions instead of a single overfit one. From that cloud, they build ensembles. First, they “retrofit” already overfit models: starting from an Adam-trained network, simmering nudges the weights away from brittle minima and recovers clean underlying structure in both regression and classification tasks. More strikingly, ab initio simmering—used from scratch, with no optimization stage—beats early stopping, dropout, and even ensembled early-stopped models on CIFAR-10 with a ConvNet, and on a Transformer-based translation task, reaching higher test accuracy in fewer epochs. Framed in information geometry, simmering is an example of sufficient training: sampling many near-equivalent parameter sets along sloppy directions, then averaging away spurious fits to noise. It suggests that for modern over-parameterized networks, sufficiency can be more powerful than optimality—and that future training algorithms may look less like classic optimization, and more like carefully designed statistical-physics experiments in parameter space. Paper: https://lnkd.in/dhCXaTfW #DeepLearning #MachineLearning #NeuralNetworks #Generalization #Overfitting #Optimization #EnsembleLearning #AIResearch #StatisticalPhysics #InformationGeometry #TrainingDynamics #SufficientTraining #Regularization #MLTheory #AIforScience

I am thrilled to share our latest work at Wand AI - "Concise Reasoning via Reinforcement Learning" - where we challenge the conventional wisdom that longer chain-of-thought outputs necessarily yield better reasoning accuracy, and seek to redefine the conversation around model efficiency in AI research. By re-examining the fundamentals of reinforcement learning (RL), we demonstrate that a more concise reasoning process can not only reduce computational costs, but also enhance performance. Paper: https://lnkd.in/g93W3WH6 In this work, we rigorously analyze how RL-based training protocols inadvertently encourage verbosity -- a design choice traditionally viewed as beneficial. Through detailed mathematical analysis, we show that this verbosity is actually an artifact of the training optimization process. To address this, we introduce a novel secondary phase of RL post-training that leverages a small, carefully selected set of problems to trim the chain-of-thought. This method systematically reduces token usage while maintaining or even improving the model’s reasoning accuracy. Our experimental evaluations provide compelling evidence supporting our theoretical insights. The models refined with our concise reasoning technique match or outperform those trained with conventional RL methods. Furthermore, the reduction in token usage leads to lower computational overhead and faster response times - key improvements for deploying large language models in practical applications. We believe this work has the potential to reshape how we approach model efficiency in AI research. By uncovering the natural correlation between conciseness and accuracy, we invite the community to rethink existing training paradigms. I am excited about the opportunities for further research and collaboration to build on these findings and drive innovations in efficient model training and deployment strategies. This work was led by Banafsheh Rafiee and Mehdi Fatemi, and is joint with Mingjie Tang and myself, as part of the Wand AI Research team. #AI #ReinforcementLearning #LLM #ConciseReasoning #MachineLearning #EfficientAI #DeepLearning #Innovation #ResearchCollaboration #ML #GenAI #RL #FineTuning #PostTraining

A lot of people think LLM training is one giant process. In reality, the model that writes code, answers questions, and follows instructions today is the result of multiple training stages stacked on top of each other. And each stage solves a completely different problem. 1] Pretraining creates capability This is where the model learns: • language • facts • grammar • reasoning patterns • world knowledge The objective is surprisingly simple: Predict the next token. Over and over. Across trillions of tokens. The output is a foundation model. Not an assistant. 2] Supervised Fine-Tuning creates behavior After pretraining, the model still does not know how to: • follow instructions • respond in chat format • call tools • follow company workflows That is where SFT comes in. The model learns from high-quality instruction-response examples. This is usually where the model starts feeling like a product. 3] Alignment creates preferences Even after SFT, behavior is still highly flexible. Alignment teaches: • safety • tone • helpfulness • refusal behavior • preference tradeoffs Techniques like: • RLHF • DPO live at this layer. Importantly: Alignment changes behavior. Not knowledge. 4] Data matters more than architecture tweaks One thing that stood out repeatedly: Most teams focus on models. The strongest teams focus on datasets. Because: • bad data survives training • noisy labels create instability • weak preference sets create poor alignment The data contract often matters more than the optimization strategy. 5] Full fine-tuning is becoming increasingly rare Today the default path is usually: • LoRA • QLoRA • other PEFT approaches Instead of updating every parameter. The advantages are: • lower cost • faster iteration • reduced forgetting • easier experimentation Which is why most practical fine-tuning pipelines start there. 6] Every stage needs different evaluation Pretraining: • loss • perplexity SFT: • task accuracy • tool-call correctness • schema adherence Alignment: • preference win rates • safety evaluations • jailbreak resistance Using one evaluation strategy across all stages creates blind spots. One thing became very obvious while studying the training pipeline: The assistant people interact with is not created by pretraining alone. It emerges from multiple layers of: • data • optimization • fine-tuning • alignment • evaluation stacked together over time. And that is why training an LLM is really a pipeline problem, not a model problem. #llm #ai #machinelearning #deeplearning #genai #finetuning #training #sft