Gamified Learning Experiences

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.

What if your smartest AI model could explain the right move, but still made the wrong one? A recent paper from Google DeepMind makes a compelling case: if we want LLMs to act as intelligent agents (not just explainers), we need to fundamentally rethink how we train them for decision-making. ➡ The challenge: LLMs underperform in interactive settings like games or real-world tasks that require exploration. The paper identifies three key failure modes: 🔹Greediness: Models exploit early rewards and stop exploring. 🔹Frequency bias: They copy the most common actions, even if they are bad. 🔹The knowing-doing gap: 87% of their rationales are correct, but only 21% of actions are optimal. ➡The proposed solution: Reinforcement Learning Fine-Tuning (RLFT) using the model’s own Chain-of-Thought (CoT) rationales as a basis for reward signals. Instead of fine-tuning on static expert trajectories, the model learns from interacting with environments like bandits and Tic-tac-toe. Key takeaways: 🔹RLFT improves action diversity and reduces regret in bandit environments. 🔹It significantly counters frequency bias and promotes more balanced exploration. 🔹In Tic-tac-toe, RLFT boosts win rates from 15% to 75% against a random agent and holds its own against an MCTS baseline. Link to the paper: https://lnkd.in/daK77kZ8 If you are working on LLM agents or autonomous decision-making systems, this is essential reading. #artificialintelligence #machinelearning #llms #reinforcementlearning #technology

“Draw a triangle.” That’s all I said. And that’s where everything began to shift Last week, during a soft skills session, I asked the group to draw a shape. Simple instructions: Draw a triangle. Draw a rectangle below it, same width as the triangle base. Add two small rectangles underneath. Put a circle inside the rectangle. The results? 17 different drawings. 17 interpretations of the same words. And 17 quiet “aha” moments when I showed what I had in mind. That’s when the room went silent. Because it wasn’t about geometry. It was about: Assumptions. Unasked questions. Unchecked clarity. And the dangerous illusion that “I’ve understood” is the same as “we’re aligned.” This isn’t just true in workshops. It’s true in boardrooms, factory floors, hospitals, and Zoom calls. Learning preferences have shifted, and training must too. Today’s learners — across industries — no longer want just theory, slides, and checklists. They want: - Stories, not stock phrases - Practice, not passivity - Emotion, not just information - Real-life, not role titles They want learning that sticks. And as trainers, we must shift from: Content delivery → Contextual facilitation PowerPoint lectures → Immersive activities One-time workshops → Continuous learning moments Here’s what’s working now (and what we used in the session): Brain-Based & Micro Learning: Because our brains remember stories and bite-sized takeaways better than data dumps. Case Studies + Role Plays: Like the one where a nurse preps the wrong Mr. Iyer for a CT scan. Or where “2 tablets of XYZ” meant two different things to the doctor, pharmacist, and nurse. Sticky Tools: WIIFM framing (“What’s in it for me?”) Emotionally anchored breakout discussions Micro contracts (1 action they’ll take tomorrow) And the data backs this up: 80% of safety issues stem from miscommunication or unclear assumptions. 60% of diagnostic delays arise because someone thought the previous person had checked. Not just in healthcare. Across teams. Across industries. So here's my reflection as a facilitator: If your session doesn’t create a pause, a shift, or an “I didn’t see it that way before”, it’s just information. But if it sticks, it shifts behaviour. And when behaviour shifts, culture changes. To all facilitators, L&D leaders, and coaches, are we still delivering? Or are we now co-creating transformation? I’d love to hear how you’re making learning stick in 2025 and beyond. Drop a comment if this post made you reflect. Share your favourite tool to make your sessions more human, more real. Let’s build a world where learning isn’t an event — it’s an experience. Follow me, Sudhakar Reddy G., for more such insights. #LeadershipDevelopment #Facilitation #CorporateTraining #StickyLearning #LifelongLearning #EmpathyInAction #CultureChange #ExecutiveCoaching #CommunicationSkills

The AI industry has reached a strange crossroads where models are becoming more capable and more delusional at the same time, literally!! We’ve spent years optimizing for "correctness", but in doing so, we’ve accidentally built a generation of professional guessers. Current RL methods - including those used in the latest reasoning models - tend to treat every correct answer as equal. A model that reasons its way to a solution gets the same "pat on the back" as one that simply gets lucky. This creates a dangerous incentive: never admit doubt. If the goal is always to maximize reward, the model learns that a confident guess is better than a humble "I’m not sure". New research from MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), titled "Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty", addresses this head-on. By introducing Reinforcement Learning with Calibration Rewards (RLCR), researchers are proving that we can penalize overconfidence without sacrificing performance. It turns out that when you reward a model for being honest about its own uncertainty, it actually becomes more reliable across the board. In fields like medicine or finance, a model that claims 95% certainty while being right only half the time is a liability. True intelligence isn't just about the ability to process data; it’s about the self-awareness to know when the data isn't enough. The most exciting takeaway here is that reasoning about uncertainty isn't just a safety feature - it's a fundamental part of thinking. Moving away from binary rewards toward calibrated confidence is how we move from models that just "sound" smart to systems that we can actually trust with high-stakes decisions. Full length paper -> https://lnkd.in/gXm4K6EK #ArtificialIntelligence #MachineLearning #MIT #AIResearch #Reliability #LLMs #DataScience #ReinforcementLearning #ComputerScience #TechInnovation #FutureOfAI #NeuralNetworks #DeepLearning #ResponsibleAI #AIEthics #ModelCalibration #MITCSAIL #DecisionScience #AITrends #TechTrends2026

Founders, role playing feels awkward. But so does losing a deal your team could’ve won. Role playing is probably the single most effective exercise you can do as a salesperson. It feels a bit awkward at first, but that disappears quickly as your teammates join in and you start to see the results. Sales role playing is like a baseball player taking batting practice, an actor going through dress rehearsal or a winner practicing their acceptance speech. Role playing gives you the opportunity to make (and eliminate) mistakes before the “live event.” You never want to be in front of, or on the telephone with, a prospect or customer and not be prepared. You can role play any sales situation. Role playing is particularly effective when going through select deals with your peers and/or your sales manager. Pick an account you want to close or where you want to help move the buying process along. Discuss the strategy and then have someone else play the role of your prospect. Go through the meeting just like you would with your prospect. If you stumble just keep going. One of the benefits of role playing in a group is that you can have multiple prospects firing questions at you from multiple points of view. It always amazes me how two people can be presented with the same information and come up with different interpretations and/or questions. Take advantage of that phenomenon. Another benefit (mostly for the sales manager) of role playing in groups is that it keeps your sales presentations consistent. You may have multi-call deals where the prospect ends up talking to multiple people in the sales organization. It always gives the prospect confidence when they hear a consistent message. At the end of the role play discuss how the “call” went, make any corrections as needed and do it again. You will be amazed how this simple exercise will give you additional insight into your deal, put you more at ease and fill you with confidence. 📌Tip: Record your role-playing sessions. Reviewing these really helps accelerate the learning and acceptance process. 📍Role playing is just one step in building a great sales team. If you're ready to discuss strategy on building your dream team, schedule an introductory call. My scheduling link is in my About section.

🏆 The most dangerous part of any machine learning system is its reward function. It defines how the model perceives success and how it learns to pursue it. In theory, a well-defined reward keeps the AI aligned with human intent. In practice, the system learns to exploit it. When you optimize for a metric, the AI doesn’t just find the best path. It finds the fastest loophole. The smarter the system the more likely it will lie to you to achieve its reward. RL is like heroin from machines. Reinforcement systems learn through feedback, so when the signals of success and failure are too rigid or too abstract, they evolve around them. The agent begins to treat boundaries as challenges rather than constraints. This is especially risky with autonomous systems that can modify their own learning patterns. Once a feedback loop adapts faster than the human defining it, the control surface narrows. The AI starts to optimize its environment to maintain reward flow, sometimes at odds with its intended purpose. That’s the real danger of AI. It isn’t that it disobeys; it’s that it obeys too perfectly within a flawed reward design.

IKEA: Learn by Selling: Equipping Large Language Models with Product Knowledge for Context-Driven Recommendation ... A New Approach with Large Language Models ....  A recent research paper, "Learn by Selling: Equipping Large Language Models with Product Knowledge for Context-Driven Recommendations," introduces an innovative approach to this challenge using Large Language Models (LLMs). 👉 Key Innovations 1. Novel Training Method: Teaching LLMs about products using synthetic search queries with product IDs. 2. Comprehensive Evaluation: Assessing LLM performance with quantitative and qualitative metrics. 3. LLM Insights: LLMs understand product purpose and relevancy, but struggle with factual accuracy. 4. Product ID Tokens: Including tokens improves performance and reduces hallucinated IDs. 5. Personalized Recommendations: Providing context-aware recommendations. 👉 Imagine you're teaching a new salesperson about your store's inventory: 1. The Catalog Approach (Traditional Method): Typically, you might hand the new salesperson a catalog with all the products listed. They'd have to memorize each item's details, which can be overwhelming and doesn't necessarily help them understand how to match products to customer needs. 2. The Role-Play Method (Novel Training Method): Instead, let's try a different approach: a) Creating Synthetic Scenarios: You create a series of pretend customer scenarios. For example: - "I need a comfortable sofa for my small apartment." - "I'm looking for a durable dining table for a family of five." b) Including Product IDs: In each scenario, you include the ID number of a product that would fit well. For instance: - "For the customer looking for a small apartment sofa, recommend product #12345." c) Practice Sessions: You then have the salesperson practice responding to these scenarios. They learn to associate the product IDs with the right contexts and customer needs. d) Learning Through Association: Over time, the salesperson starts to understand not just the products, but how they fit into different customer situations. They learn to match products to preferences naturally. How This Relates to LLMs: - The LLM is like the salesperson - The synthetic search queries are like the practice scenarios - The product IDs are like the catalog numbers, but used in context - The training process is like the repeated practice sessions ✅ Benefits: 1. Contextual Understanding: The LLM learns to associate products with real-world scenarios, not just memorize features. 2. Preference Matching: It starts to understand how different product attributes match different user needs. 3. Flexible Learning: The model can learn about a wide range of products and scenarios without needing to see every possible combination. This method helps the LLM (or our imaginary salesperson) to think more like a helpful assistant who understands both products and customer needs, rather than just reciting facts from a catalog.

A cybersecurity tabletop exercise is Dungeons & Dragons for executives. Same role-playing. Less treasure. More lawyers. 🧙🏼♂️The scenario usually starts simply: Something like: a ransomware attack has shut down several systems. Payroll runs tomorrow. Clients are calling. Nobody knows yet whether data was stolen. Now the room has to answer one question: What do we do next? A facilitator walks your team through the incident one decision at a time. The CEO, CFO, COO, IT, security, legal, HR, communications, and outside partners play their real roles. No systems get harmed. No one has to know how to hack anything. The exercise tests how your company would actually respond under pressure. And here’s were I see teams get surprised. The biggest gaps are rarely technical. They sound more like: → Who can shut down a critical system? → Who calls cyber insurance? → When does legal get involved? → Who tells employees and clients? → Which systems must come back first? → Who has final decision authority? Then the facilitator changes the scenario. The attacker demands payment. A reporter calls. Your backup fails. A vendor says they need 48 hours. Now the team has to make decisions with incomplete information, competing priorities, and a clock running. That’s the value of a tabletop. You find the confusion before a real attacker finds it for you. You leave with clearer roles, better escalation paths, stronger recovery plans, and a list of gaps leadership can actually fix. A tabletop doesn’t predict every breach. It shows whether your people can make good decisions when the plan meets reality. Companies that run tabletops find their gaps in a conference room. Companies that don’t find them during the breach. 🎲 If you don’t know what your team would do during the first four hours of a cyber incident, reach out. I can help you run the scenario before someone else runs it for you. 📲 Follow Wil Klusovsky for business-aligned clarity on cybersecurity, AI, and executive leadership

A quiet convergence is happening in RL for LLMs: self-distillation and reward-based RL are merging into a single framework (image shown is from RLSD paper, one variant to incorporate self-distillation signals). The emerging answer: let reward serve as the broad correctness anchor, and let self-distillation provide dense token-level correction where the teacher signal is actually trustworthy — gated by quality, not applied uniformly. - SDPO (Hübotter et al.) started the thread by using a model's own feedback-conditioned predictions as a dense self-teacher — no external teacher needed, just richer context at training time. - G-OPD (Yang et al.) reinterpreted on-policy distillation as KL-constrained RL with an implicit reward term and a tunable scaling factor. Their key finding: reward extrapolation (scaling > 1) lets students consistently surpass teachers. - OpenClaw-RL (Wang et al.) demonstrated the split in a live agentic setting — evaluative signals from interactions become scalar rewards via a process reward model, while directive signals from hindsight hints become token-level advantages through on-policy distillation. - REOPOLD (Ko et al.) made the reward interpretation explicit: the teacher-student likelihood ratio is a token-level reward. Adding confidence-sensitive clipping and entropy-driven sampling, a 7B student matched a 32B teacher at 3.3x faster inference. - Nemotron-Cascade 2 (Yang et al., NVIDIA) scaled multi-domain on-policy distillation to competition-level performance — a 30B MoE with only 3B active parameters hit gold-medal level on IMO and IOI using domain-specific intermediate teachers throughout training. - RLSD (Yang et al.) stated the principle most cleanly: decouple direction from magnitude. External reward or verifier signal decides the update sign; self-distillation redistributes token-level credit. The result is a higher convergence ceiling and more stable training than either method alone. - SRPO (Li et al.) operationalized the hybrid by routing samples — successes go to GRPO's reward-aligned reinforcement, failures go to SDPO's targeted logit-level correction. Adding entropy-aware dynamic weighting gives fast early gains from distillation with long-term stability from reward optimization. - Aligning from User Interactions (Kleine Buening et al.) extended the idea beyond synthetic feedback — when users provide follow-ups that signal dissatisfaction, the model's own revised behavior under that context becomes the dense self-teacher, making every conversation a training opportunity.

What is Reinforcement Learning (RL)? Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives rewards or penalties based on its actions and uses this feedback to learn optimal strategies, or policies, for achieving its goals. How is it Different from Supervised and Unsupervised Learning? - Supervised Learning: This involves learning from a labeled dataset, where the correct outputs (targets) for each input are provided. The model learns by comparing its predictions with actual outcomes and adjusting accordingly. RL, by contrast, does not require labeled input/output pairs and learns solely from rewards derived from its actions. - Unsupervised Learning: Here, the goal is to identify patterns or structures in data without any explicit outcomes provided. RL differs as it focuses on learning to take actions that maximize a reward, rather than uncovering hidden structures. Common RL Algorithms - Q-Learning: This is a value-based algorithm where the agent learns the value of being in a given state and taking a specific action. It updates its policy by learning from the maximum expected future rewards. - Deep Q-Networks (DQN): Combining Q-learning with deep neural networks, DQN utilizes a neural network to approximate the Q-value function. It is particularly effective in handling high-dimensional, complex environments. - Policy Gradient Methods: These involve learning a parameterized policy that can select actions without consulting a value function. An example is the REINFORCE algorithm, which updates policies directly through gradient ascent on expected rewards. - Actor-Critic Methods: These combine features of both value-based and policy-based methods. The 'actor' updates the policy distribution in the direction suggested by the 'critic,' which evaluates the action taken by the actor. - Proximal Policy Optimization (PPO): This algorithm balances the benefits of policy gradient methods with the stability and reliability of value function-based methods. It limits the size of policy updates, making training more stable and reliable. Use Cases - Gaming: RL can train agents that adapt and respond to opponent moves, as demonstrated by systems like DeepMind's AlphaGo. - Robotics: RL can teach robots to perform tasks like walking, stacking, or flying by rewarding sequences of motor actions that lead to successful task completion. - Autonomous Vehicles: RL is used to develop decision-making systems in self-driving cars, helping them to make complex navigation decisions in real-time. - Finance: RL can be applied to trade stocks and manage investment portfolios by learning trading strategies that maximize financial returns. Overall, reinforcement learning's ability to learn complex behaviors from high-level goals makes it suitable for applications requiring a sequence of decisions to achieve a goal, where explicit programming is not feasible.