Advancing LLM Capabilities Without Retraining

Large language models (LLMs) can improve their performance not just by retraining but by continuously evolving their understanding through context, as shown by the Agentic Context Engineering (ACE) framework. Consider a procurement team using an AI assistant to manage supplier evaluations. Instead of repeatedly inputting the same guidelines or losing specific insights, ACE helps the AI remember and refine past supplier performance metrics, negotiation strategies, and risk factors over time. This evolving “context playbook” allows the AI to provide more accurate supplier recommendations, anticipate potential disruptions, and adapt procurement strategies dynamically. In supply chain planning, ACE enables the AI to accumulate domain-specific rules about inventory policies, lead times, and demand patterns, improving forecast accuracy and decision-making as new data and insights become available. This approach results in up to 17% higher accuracy in agent tasks and reduces adaptation costs and time by more than 80%. It also supports self-improvement through feedback like execution outcomes or supply chain KPIs, without requiring labeled data. By modularizing the process—generating suggestions, reflecting on results, and curating updates—ACE builds robust, scalable AI tools that continuously learn and adapt to complex business environments. #AI #SupplyChain #Procurement #LLM #ContextEngineering #BusinessIntelligence

For years, fine-tuning LLMs has required large amounts of data and human oversight. Small improvements can disrupt existing systems, requiring humans to go through and flag errors in order to fit the model to pre-existing workflows. This might work for smaller use cases, but it is clearly unsustainable at scale. However, recent research suggests that everything may be about to change. I have been particularly excited about two papers from Anthropic and Massachusetts Institute of Technology, which propose new methods that enable LLMs to reflect on their own outputs and refine performance without waiting for humans. Instead of passively waiting for correction, these models create an internal feedback loop, learning from their own reasoning in a way that could match, or even exceed, traditional supervised training in certain tasks. If these approaches mature, they could fundamentally reshape enterprise AI adoption. From chatbots that continually adjust their tone to better serve customers to research assistants that independently refine complex analyses, the potential applications are vast. In today’s AI Atlas, I explore how these breakthroughs work, where they could make the most immediate impact, and what limitations we still need to overcome.

Most LLM agents stop learning after fine-tuning. They can replay expert demos but can’t adapt when the world changes. That’s because we train them with imitation learning—they copy human actions without seeing what happens when they fail. It’s reward-free but narrow. The next logical step, reinforcement learning, lets agents explore and learn from rewards, yet in real settings (e.g. websites, APIs, operating systems) reliable rewards rarely exist or appear too late. RL becomes unstable and costly, leaving LLMs stuck between a method that can’t generalize and one that can’t start. Researchers from Meta and Ohio State propose a bridge called Early Experience. Instead of waiting for rewards, agents act, observe what happens, and turn those future states into supervision. It’s still reward-free but grounded in real consequences. They test two ways to use this data: 1. Implicit World Modeling: for every state–action pair, predict the next state. The model learns how the world reacts—what actions lead where, what failures look like. 2. Self-Reflection: sample a few alternative actions, execute them, and ask the model to explain in language why the expert’s move was better. These reflections become new training targets, teaching decision principles that transfer across tasks. Across eight benchmarks, from home simulations and science labs to APIs, travel planning, and web navigation, both methods beat imitation learning. In WebShop, success jumped from 42 % to 60 %; in long-horizon planning, gains reached 15 points. When later fine-tuned with RL, these checkpoints reached higher final performance and needed half (or even one-eighth) of the expert data. The gains held from 3B to 70B-parameter models. To use this yourself:, here is what you need to do: • Log each interaction and store a short summary of the next state—success, error, or side effect. • Run a brief next-state prediction phase before your normal fine-tune so the model learns transitions. • Add reflection data: run two-four alternative actions, collect results, and prompt the model to explain why the expert step was better. Train on those reflections plus the correct action. • Keep compute constant—replace part of imitation learning, not add more. This approach makes agent training cheaper, less dependent on scarce expert data, and more adaptive. As models learn from self-generated experience, the skill barrier for building capable agents drops dramatically. In my opinion, the new challenge is governance and ensuring they don’t learn the wrong lessons. That means filtering unsafe traces, constraining environments to safe actions, and auditing reflections before they become training data. When rewards are scarce and demonstrations costly, let the agent learn from what it already has, its own experience! That shift turns LLMs from static imitators into dynamic learners and moves us closer to systems that truly improve through interaction, safely and at scale.

Did Stanford just kill LLM fine-tuning? . . This new paper from Stanford, called Agentic Context Engineering (ACE), proves something wild: you can make models smarter without changing a single weight. Here's how it works: Instead of retraining the model, ACE evolves the context itself. The model writes its own prompt, reflects on what worked and what didn't, then rewrites it. Over and over. It becomes a self-improving system. Think of it like the model keeping a living notebook where every failure becomes a lesson and every success becomes a rule. The results are impressive: - 10.6% better than GPT-4-powered agents on AppWorld - 8.6% improvement on financial reasoning tasks - 86.9% lower cost and latency No labeled data required. Just feedback loops. Here's the counterintuitive part: Everyone's chasing short, clean prompts. ACE does the opposite. It builds dense, evolving playbooks that compound over time. Turns out LLMs don't need simplicity. They need context density. The question here is how to manage all this information and experience. This is where building a real-time memory layer for Agents like Zep AI (YC W24) can be a great solution and active area of research going forward. What are your thoughts? I have linked the paper in the next tweet! ____ If you found it insightful, reshare with your network. Find me → Akshay Pachaar ✔️ For more insights and tutorials on LLMs, AI Agents, and Machine Learning!

Good paper summarizing Context Engineering. The paper is 166 pages but only 60 pages content, rest references Context Engineering is a formal discipline focused on the systematic optimization of information payloads for Large Language Models (LLMs) during inference. It moves beyond simple prompt design by treating the input context (C) not as a static string, but as a dynamically structured set of informational components that are sourced, filtered, formatted, and orchestrated. The field is broken down into two main categories: 1) Foundational Components: These are the core technical capabilities for handling context:     a) Context Retrieval and Generation: This involves creating effective instructions (prompt-based generation) and acquiring external knowledge from various sources. Techniques include prompt engineering and external knowledge retrieval, such as from knowledge graphs.     b) Context Processing: This component focuses on transforming and optimizing acquired information. It deals with handling long sequences, enabling LLMs to refine their own outputs, and integrating structured and multimodal information.     c) Context Management: This addresses the efficient organization, storage, and utilization of contextual information, including managing memory hierarchies, applying compression techniques, and working within context window constraints. 2) System Implementations: These are architectural integrations of the foundational components to create sophisticated AI systems:     a) Retrieval-Augmented Generation (RAG): Combines LLMs' internal knowledge with external retrieved information.     b) Memory Systems: Enable persistent interactions and allow LLMs to maintain state across conversations, overcoming their inherent statelessness.     c) Tool-Integrated Reasoning: Allows LLMs to use external tools for function calling and interacting with environments, addressing limitations like outdated knowledge or calculation inaccuracy.     d) Multi-Agent Systems: Involve coordinating communication and orchestration among multiple LLM agents. The purpose of Context Engineering is to enhance LLM performance, optimize resource usage, and unlock future potential for LLM applications. It is essential because while LLMs are proficient at understanding complex contexts when augmented by advanced context engineering, they still face challenges, particularly in generating equally sophisticated, long-form outputs. The discipline helps mitigate issues like hallucinations, unfaithfulness to input, and sensitivity to input variations. It shifts the focus from the "art" of prompt design to the "science" of information logistics and system optimization. Think of Context Engineering as an advanced AI operating system for LLMs. Just as an operating system manages a computer's memory, processes, and external devices to run applications efficiently