Interaction Feedback Mechanisms

"Seen as complex, adaptive, and dynamic systems, groups: • Are nested open systems. Groups interact with the smaller systems (i.e., the members) embedded within them and the larger systems (e.g., organizations, communities) within which they are embedded; • Have fuzzy boundaries that both distinguish them from and connect them to their members and their different contexts — organizations, communities, and physical and cultural environments; • Change their structure and behaviour over time, yielding temporal patterns of development. Change is driven in part by the effects of experience and history, and in part by the group’s adaptive response to the impact of events; • Contain feedback loops that create non-linear effects. Both negative (damping) and positive (amplifying) feedback are always found in groups as complex systems. A small change in a local variable that triggers a positive feedback loop can ultimately result in a big change at the global level; • Are shaped by unobservable, but influential, emergent structures and properties. Interactions between members are based on the idea of coordination — members in a group must adjust to one another interpersonally to coordinate goals, understanding, and action. As a result of many cycles of interaction, patterns emerge that give rise to group-level properties and structures that define the overall dynamic of the group. Influential variables in a group can include written and unwritten norms that dictate behaviour, expectations about member’s roles, and networks of connections among the members (like status, attraction and communication networks)." By Daniel Christian Wahl. #selforganization #complexity #systemsthinking --- tijntjoelker.substack.com 💌

This paper is a big deal! It's well known that RL works great for math and code. But RL for training agents is a different story. The default approach to training LLM agents today is based on methods like ReAct-style reasoning loops, human-designed workflows, and fixed tool-calling patterns. The issue is that these methods treat the environment as passive rather than interactive. But in the real world, agents must make sequential decisions, maintain memory across turns, and adapt to stochastic environmental feedback. That's fundamentally an RL problem. This new research introduces Agent-R1, a framework for training LLM agents with end-to-end reinforcement learning across multi-turn interactions. As agents move from predefined workflows to autonomous interaction, end-to-end RL becomes the natural training paradigm. Agent-R1 provides a modular foundation for scaling RL to complex, tool-using LLM agents. Standard RL for LLMs assumes deterministic state transitions. You generate a token, append it to the sequence, done. But agents trigger external tools with uncertain outcomes. The environment responds unpredictably. State transitions become stochastic. Therefore, the researchers extend the Markov Decision Process framework to capture this. State space expands to include full interaction history and environmental feedback. Actions can trigger external tools, not just generate text. Rewards become dense, with process rewards for intermediate steps alongside final outcome rewards. Two core mechanisms make this work. An Action Mask distinguishes agent-generated tokens from environmental feedback, ensuring credit assignment targets only the agent's actual decisions. A ToolEnv module manages the interaction loop, handling state transitions and reward calculation when tools are invoked. On multi-hop question answering, RL-trained agents dramatically outperform baselines. The weakest RL algorithm (REINFORCE++) still beat naive RAG by 2.5x on average EM. GRPO achieved 0.3877 average EM compared to 0.1328 for RAG. Ablation results also confirm that the design matters. Disabling the advantage mask dropped PPO performance from 0.3719 to 0.3136. Disabling the loss mask caused further degradation to 0.3022. Precise credit assignment is essential for multi-turn learning.

Aligning AI with human preferences typically requires collecting a lot of explicit feedback, which can be costly and not reflective of real-world usage. But there are many signals already embedded in our everyday interactions with AI. It turns out that the casual “thanks” or “wait a sec” moments in a chat can be just as valuable when training a model as formal ratings – if we know how to use them. 📖 WildFeedback: Aligning LLMs With In‑situ User Interactions and Feedback (https://lnkd.in/gxGyb-ig), by Taiwei Shi, Zhuoer Wang, Longqi Yang, Ying-Chun Lin, Zexue He, Mengting Wan, Pei Zhou, Sujay Kumar Jauhar, Sihao Chen, Freddie Zhang, Jieyu Zhao, Xiaofeng Xu, Xia Song, and Jennifer Neville. NeurIPS 2024 Workshop. What’s novel in this paper is not just that it incorporates human feedback, but how it does so. The authors turn weak, messy signals from real conversations (implicit cues like “thanks,” “wait,” or “revise this”) into clean preference pairs at scale, and then show those signals can actually nudge the model in the right direction. This reframes alignment from a one‑off RLHF sprint into an ongoing, in‑situ dialog with users. The paper is exceptionally well grounded in real data (mining 20,281 preference pairs from 148,715 multi‑turn chats), and complements the usual benchmark tests with a checklist‑guided evaluation. A good template if you’re thinking about continuous AI alignment in everyday use. #BeyondTheAbstract #NeurIPS2024 #AIAlignment #OAR #AppliedResearch

Picture this. It's your birthday and your best friend shows up on your front porch with a cute red present. You unwrap it. It's a shoe box. You love shoes. Your mom knows. Your sister knows. Heck, even your cat knows. You open it. You see shoes. New shoes. *record scratch* Nike shoes??? Your lock screen wallpaper is literally the pair of New Balance kicks you've been drooling over for months. You've been tweeting about it at least once a week since Easter. If only they listened, right? This is what happens when you assume you know your audience. Yes, they like shoes, but what brand? What's their shoe size? Favorite color? How about accessories? Socks, maybe? Do they fancy plain or striped ones? How about a shoe-cleaning kit? How about no more assumptions? Never assume you know what your audience wants. And the only way not to assume is to ✨RESEARCH and LISTEN.✨ Audience RESEARCH is the equivalent of knowing your friend likes running shoes and LISTENING is the equivalent of finding out exactly which ones they like. When you solely focus on surface-level info like someone's love for shoes, you'll miss out on important details that: → help you filter their interests, and → make them feel seen In the business-customer context, you can listen via: 1️⃣ Social media interactions Comments and discussions hold a lot of valuable customer information. Find out: → What questions they're asking → What the recurring problem is → How your product/service solves it 2️⃣ Feedback Track the most frequent complaints or commendations. You can find these in your: → Reviews → Surveys → Testimonials → Support tickets → Social media comments 3️⃣ Direct interviews The best way to know is by asking. There's only so much you can tell from the outside or with a research tool. Host focus groups or 1:1 conversations to gather real-time responses. Ask: → Why do they love your product/service? → Why don't they love it? → Which of their preferences have evolved? So, the next time you're planning a campaign or strategy, think about how it feels to receive a gift that would have been perfect if the gifter actually listened.

Reinforcement Learning from Human Feedback (RLHF) is transforming Large Language Models (LLMs) by establishing a feedback loop that significantly enhances their performance beyond traditional training methods. Unlike standard supervised learning, which relies on static datasets, RLHF integrates real-time human input into reinforcement signals, enabling LLMs to better grasp language subtleties and user intent. For example, OpenAI's ChatGPT continually refines its responses based on user interactions, resulting in more contextually relevant and user-aligned exchanges. This adaptive approach is crucial for bias mitigation, addressing harmful stereotypes related to race, gender, and other factors. When users identify biased outputs or provide corrective feedback, the model learns and improves continuously, reducing the risk of reinforcing these biases. Techniques such as Proximal Policy Optimization (PPO) help balance competing objectives, ensuring that LLMs not only foster creativity but also uphold factual accuracy. In creative writing, RLHF empowers models to generate imaginative content that resonates with readers while remaining grounded in realistic contexts. Future developments in RLHF may explore hybrid models that combine human feedback with automated systems, creating a scalable framework for refining LLM outputs. This evolution is essential for enhancing the quality of AI-generated content and promoting ethical standards, including fairness, accountability, and transparency. Ultimately, RLHF facilitates more effective machine-human interactions, allowing AI systems to adapt intuitively while prioritizing user trust and ethical responsibility. TL;DR: RLHF uses human feedback to make LLMs more accurate, ethical, and responsive to user intent. #RLHF #LLMs #AI #MultiObjectiveOptimization #Ethics #HumanFeedback #ScalableAI

Courts do not scale by hiring more judges. They scale by turning precedent into predictable rule. The same mechanism works for autonomous systems. The second companion paper in the Architecting Autonomy series introduces the case law feedback loop: when the governance layer encounters a conflict it cannot resolve deterministically, the conflict escalates to human judgment. The resolution is encoded back into the governance layer. That conflict class is handled deterministically next time. The 1% of interactions requiring human judgment shrinks as the treaty layer thickens. The paper defines four arbitration patterns for resolving conflicts between governed agents: scope-based (most specific authority governs), priority (contextual ordering per interaction class), conjunction (both domains must permit), and state-aware (unconfirmed state triggers contraction, not permission). Each is evaluated deterministically. Each produces a legibility record. But the patterns are necessary, not sufficient. What makes the governance layer mature is the case law feedback loop: escalation → human resolution → encoding → verification before deployment → promotion to deterministic handling → revocation if conditions change. The governance layer learns. An organisation that has operated this architecture for a year has a broader governed scope, lower escalation volume, and faster operation than one that deployed last month. A reference implementation exists: all four patterns are deployed as a deterministic governance engine, architecturally separate from the agents it governs. The patterns described here are not theoretical. They are implemented. An instructional tutorial walks through the code step by step. https://lnkd.in/gFavfm2J One finding from the implementation that changed our model: governance is not invisible to the agent. Deny and escalate results feed back into the reasoning layer. The agent observes governance outcomes and adjusts. Governance is part of the environment, not a cage around it.

Most Customer Feedback Never Changes Anything. And your customers know it. Customer forums. User groups. Feedback sessions. Advisory boards. They sound like strong Customer Success strategy… But too often, they become: → Performative listening → One-way communication → Or worse… a backlog graveyard Here’s what nobody wants to hear: Customer forums are not about collecting feedback. They are about shaping outcomes. The best Customer Success leaders use these mechanisms to: ✔ Turn scattered feedback into prioritized, actionable insights ✔ Influence product roadmaps with real customer evidence—not opinions ✔ Create shared ownership between CS, Product, and Commercial teams ✔ Close the loop visibly so customers see their impact ✔ Build community-driven value, not just company-driven messaging Because when done right… Customer forums don’t just “engage customers”— They turn customers into co-creators of value. And that changes everything: → Stronger retention → Faster adoption → More credible expansion conversations → Products that actually solve real problems 🚫 The mistake: Treating forums as events ✅ The opportunity: Treating them as strategic inputs to your operating model If your customer feedback isn’t shaping decisions, you don’t have a feedback strategy… You have a listening problem.

Ever logged on, seen a special event like today’s glowing World Quantum Day Doodle, and wondered what actually happens when you interact with it? 🤔✨ Let’s look beyond the spinning Bloch spheres and tell the story of a single clickand how it fuels the world's largest machine learning ecosystem. 🌍👇 ⚡ Chapter 1: The Spark (Event Capture) You see the Doodle. You pause, hover, and maybe click to learn more about how a qubit exists in a superposition. At that exact millisecond, an invisible net catches your action. 🕸️ JavaScript and Google Tag Manager log your interaction as a distinct, timestamped event, gathering it alongside over 100 other raw signals like your scroll depth and dwell time. ⏱️ 🌊 Chapter 2: The Fast River (Data Ingestion) Your click doesn't just sit in a database; it hits the rapids! 🚣♂️ It’s instantly published as a real-time message to Cloud Pub/Sub—the massive front door of Google's pipeline. From there, Dataflow sweeps it up, cleaning, deduplicating, and enriching your interaction in mere milliseconds. 🚀 🏛️ Chapter 3: The Grand Library (Storage & Feature Engineering) Next, your data is routed to the archives. If it's needed for a low-latency, real-time prediction, it sprints to Bigtable. 🚄 If it's being saved for massive batch analytics, it settles into BigQuery. 🗄️ But raw clicks are messy. The Vertex AI Feature Store steps in to transform your simple interaction into a polished, standardized mathematical "feature" that ML models can actually understand. This step is the superhero that prevents the dreaded training-serving skew. 🦸♂️ 🧠 Chapter 4: The Brain Adapts (Model Training) This is where your click becomes a teacher. 🧑🏫 Ranking models look at your interaction as implicit feedback. You clicked the Quantum Day link and stayed to read the results? The model registers a positive signal—a "reward" 🏆—and learns to rank that content higher for future users. It’s the exact same Reinforcement Learning from Human Feedback (RLHF) concept powering today’s advanced LLMs! 🤖 🛡️ Chapter 5: The Watchful Guardian (Continuous Retraining) But what happens next week when everyone stops clicking on quantum physics? The system doesn't wait for an engineer to notice. 📉 TensorFlow Data Validation (TFDV) constantly watches the stream of incoming events. 👁️ When it spots a shift in human behavior (data drift), it automatically triggers an automated retraining pipeline. The machine adapts on its own. 🔄 🎬 The Epilogue The next time you see a special event or a custom Doodle, remember: you aren't just looking at a clever piece of art. 🎨 Your interaction is the opening scene of a massive, globally distributed, self-improving machine learning loop. ♾️🚀 #MachineLearning #MLOps #SystemDesign #AIEngineer #DataEngineering #AIResearch #GoogleCloud #DataPipelines #ArtificialIntelligence Google Google DeepMind

Reinforcement Learning(RL) has quietly become one of the most important techniques shaping the evolution of LLM fine-tuning. For years, we optimized models through supervised learning, predicting the next token or minimizing cross-entropy loss. But as generative models scaled, we needed them to reason, align with intent, and adapt to human feedback in more complex ways. That’s where Reinforcement Learning (RL) entered the picture. At its core, RL is about interaction and feedback. An agent learns by interacting with an environment to maximize reward. In the context of large language models, the agent is the model itself. Each action is the next token it generates, and the reward is a signal derived from metrics or human preferences that measures how aligned the output is with the desired goal. Here’s a quick technical primer on the RL methods now powering GenAI fine-tuning: 1. RL Fine-Tuning (RLFT) We adapt a pre-trained model to new objectives like truthfulness, coherence, and safety using policy gradient algorithms such as PPO (Proximal Policy Optimization). Instead of minimizing loss, the model improves through iterative reward-driven optimization. 2. Reinforcement Learning from Human Feedback (RLHF) Human preference data trains a Reward Model (RM), which then guides fine-tuning through PPO. RLHF was key in aligning early LLMs, making outputs more helpful, factual, and instruction-following. 3. Direct Preference Optimization (DPO) A newer, more efficient approach. DPO skips the Reward Model and the full RL loop. It reframes alignment as a direct optimization task, teaching the model to prefer human-approved responses through a simplified objective function. It’s computationally stable, theoretically grounded in RL, and rapidly becoming a standard for GenAI alignment. Reinforcement Learning is no longer just a research concept. It is the foundation of how large language models learn to reason, align, and self-improve. ♻️ Share this with your network to spread learning 🔔Follow me for more data and AI insights

Think about the best customer service experience you’ve ever had. The issue was resolved quickly, your input mattered, and you left with more trust in the organization. Now, imagine if government services worked the same way… This doesn’t happen by accident. It requires intention. That’s what Closed-Loop Feedback (CLF) brings— it is an intentional operational customer experience framework based on industry best practice that ensures real-time responsiveness and long-term accountability to the people the organization serves. This has been the journey of customer experience team efforts that started under the first Trump administration— and there are great examples of agencies putting these practices in place and improving service delivery efficiency, billions in cost avoidance, reducing cost to serve, and greater impact to the public as a result. But so much more can be done, we have only scratched the surface… so much more can be done building on the foundations of goodness with this intentional approach… The Closed-Loop Feedback Model is an operational accountability framework that creates a continuous cycle of improvement, where real-time data drives decisions, inefficiencies are identified and addressed, and trust is rebuilt through transparency. 🔄 Micro Loop – Addresses feedback in real-time, ensuring that individual concerns are heard and resolved quickly. This prevents small issues from becoming systemic failures. 🚀 Macro Loop – Uses insights from frontline interactions to drive broader policy improvements, operational efficiencies, and service innovations. This ensures agencies evolve based on actual citizen needs, not just assumptions. By implementing Closed-Loop Feedback as part of its service delivery, government will: - Improve efficiency and effectiveness by streamlining services based on real user input. - Increase productivity by focusing resources on what matters most. - Enhance service quality through continuous iteration and innovation. - Strengthen public trust by demonstrating transparency and responsiveness. This approach modernizes government service delivery, ensuring agencies act on citizen needs. It is how we move from a reactive system to one that is responsive and proactively delivers better experiences, stronger infrastructure, and real impact for the people we serve. The future of government is citizen driven. Closing the loop builds trust and ensures the efficient and effective service delivery that citizens deserve. Thank you to all the dedicated government employees that have been part of this movement. #Leadership #Management #CustomerExperience #CX #ServiceDelivery #Accountability #Efficiency #Innovation #Modernization #Government