Learning & Adaptation
7 篇文章
Agent Reinforcement Fine-Tuning (Agent RFT)
After optimizing prompts and task design, agents may still underperform on your specific business tasks because: - **Domain shift**: Your tools and business context differ from what the base model wa…
emergingCompounding Engineering Pattern
Traditional software engineering has **diminishing returns**: each feature added increases complexity, making subsequent features harder to build. Technical debt accumulates, onboarding takes longer, …
emergingFrontier-Focused Development
AI capabilities are advancing so rapidly that products optimized for today's models will be obsolete in months. Many teams waste time solving problems that new models already solve, or build products …
emergingMemory Reinforcement Learning (MemRL)
LLMs struggle with **runtime self-evolution** due to the stability-plasticity dilemma: - **Fine-tuning**: Computationally expensive and prone to catastrophic forgetting - **RAG/memory systems**: Rely…
proposedShipping as Research
In the rapidly evolving AI landscape, waiting for certainty before building means you're always behind. Traditional product development emphasizes validation and certainty before release, but when the…
emergingSkill Library Evolution
Agents frequently solve similar problems across different sessions or workflows. Without a mechanism to preserve and reuse working code, agents must rediscover solutions each time, wasting tokens and …
establishedVariance-Based RL Sample Selection
Not all training samples are equally valuable for reinforcement learning: - **Zero-variance samples**: Model gets same score every time (always correct or always wrong) → no learning signal - **Waste…
emerging