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All digests tagged Continual Learning
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Continual learning is becoming boring, in a good way: it is the software development life cycle, applied to models. Model development requires systematic practices like versioning, APIs, and robust evaluation (Evals).
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The continual learning loop is unsolved. We must balance SFT (knowledge injection) and RL (behavior optimization) by carefully tuning the SFT/Distillation to RL ratio for optimal model performance.
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To own your agent's intelligence, focus on three questions: where it fails, how to reproduce it, and how to fix it. This requires building robust 'worlds' and 'tasks' that mimic production.
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Improving agents is a data mining problem. The key to continuous improvement is collecting comprehensive agent traces, which serve as the substrate for observability and continual learning.
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This analysis explores how ChatGPT and Claude implement memory via running profiles. The speaker argues that memory is a function of compute and cannot be outsourced.
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Learn how Applied Compute brings continual learning to enterprises using a 2x2 distillation spectrum. Discover how online hinting and per-step guidance can boost agent performance from 15% to 80% without golden answers.
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Yu Su argues that scaling AI requires moving beyond raw intelligence. True capability comes from 'Expertise,' which is achieved through continual learning to master specialized, complex domains.
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Scaling Compute on Context addresses how to give LLMs deep expertise from private data when public scaling fails. It reviews methods like KV compaction and distillation to achieve self-improvement.
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Will Brown discusses extending Reinforcement Learning (RL) into complex, real-world tasks that lack clean, verifiable rewards. The core thesis is that 'environments' must serve as the anchor for learning.
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Discover Continual Learning: The key to building adaptive AI systems. We explore how CL mitigates catastrophic forgetting and data drift using a real-world case study in MRI diagnostics.
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Engram discusses why long context and RAG break down when dealing with trillions of proprietary tokens. Solutions involve 'cartridges' for knowledge compression and 'test-time training' to build true AI intuition.
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