Tag: AI

All the articles with the tag "AI".

• Improving LLM Internationalization: Bridging the Gap in Tool Use and Agency

  10 Mar, 2026 · 17 min read

  LLMs achieve 57% tool-calling accuracy in English but only 34% across 52 languages — and 6.8% for the worst. This post covers the full playbook for closing the multilingual gap: training-time techniques, agentic architecture patterns, failure mode analysis, and RL-based approaches for i18n.

• The Unverifiable Reward Problem: The Real Frontier of RL for LLMs

  7 Mar, 2026 · 11 min read

  Deep research on tasks with unverifiable rewards in RL — the key bottleneck for scaling RL beyond math and code. Covers JEPO, NRT, RLNVR, self-play methods, GenRM, Constitutional AI, reward hacking mitigation, and more.

• Instruction Following: What Models Get Wrong and How to Fix It with Better Post-Training Data

  4 Mar, 2026 · 36 min read

  LLMs can write poetry and solve math, but ask them to 'respond in exactly 3 bullet points using only lowercase' and they stumble. This post dissects the taxonomy of instruction-following failures and provides a practical playbook for building post-training data that actually fixes them.

• Experience-Augmented In-Context Learning: A Training-Free Complement to RL Post-Training

  28 Feb, 2026 · 23 min read

  RL post-training makes models smarter, but it can't cover the infinite long tail of real-world cases. Experience-augmented ICL retrieves successful reasoning traces at inference time, letting agents learn continuously from real usage — no retraining required.

• Tool Selection Optimization for LLM Agents at Scale

  9 Jan, 2026 · 18 min read

  A deep technical dive into tool selection—retrieval strategies, context optimization, learned selection, and the engineering trade-offs that matter when scaling to hundreds of tools.

• Generative Engine Optimization (GEO): How to Get Your Product Cited by AI

  2 Jan, 2026 · 13 min read

  A comprehensive guide to Generative Engine Optimization—making your content retrievable, citable, and recommendable by large language models.