(pronounced "little" or "L-L-M-L") is the discipline of controlling, optimizing, and auditing the reasoning path of Large Language Models. While standard ML focuses on data and weights, LLML focuses on intent, inference, and integrity .
This loop adapts the model to a specific new task using a small amount of data, leveraging meta-knowledge to make fast adjustments. 2. The Outer Loop (Knowledge Accumulation) (pronounced "little" or "L-L-M-L") is the discipline of
: The definitive, frequently updated living document on arXiv covering model architectures, training data, and evaluation benchmarks. Below are some of the most comprehensive and
The search for "llml" suggests you are likely referring to (Large Language Models) . Below are some of the most comprehensive and authoritative articles covering how they work, their current state in 2026, and their limitations. 🛠️ Foundational & Explainer Articles their current state in 2026
Layer 5: Observability → LangSmith, Weights & Biases Layer 4: Orchestration → LangChain, LlamaIndex, DSPy Layer 3: Guardrails → Guardrails AI, NeMo, Rebuff Layer 2: Inference → vLLM, TGI, Ollama Layer 1: Models → Llama 3, GPT-4o, Mistral, Gemma
