Post-Training

Recommended Posts

• The Mercor Breach: What 4TB of Stolen Data Reveals About How Frontier AI Labs Actually Train Models

  12 Apr, 2026 · 22 min read

  A $10B AI data vendor was breached, exposing 84 Airtable workspaces of training data for OpenAI, Anthropic, Apple, Amazon, and Meta. This post analyzes what the public reporting reveals about each lab's evaluation methodology — rubric design, RLHF pipelines, and quality control — and what it means for the industry.

• 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.

• Adding Ads in LLM/Chatbot: Character Training for Monetization

  1 Jan, 2026 · 4 min read

  Exploring how to integrate ads in LLMs through character training—making recommendations genuinely helpful rather than annoyingly promotional.

• RLHF from an Engineering Perspective: PPO, GRPO, DPO, and Tool-Use Implementation

  30 Dec, 2025 · 12 min read

  A practical engineering guide to RLHF implementation—covering PPO, GRPO, DPO, and tool-use training with code snippets and debugging tips.

• Post-Training Is Not 'One Algorithm': Objective Functions and Implementation Essentials for PPO / DPO / GRPO

  30 Dec, 2025 · 12 min read

  Reading notes on RLHF covering PPO, DPO, and GRPO—understanding post-training as an engineering pipeline rather than a single algorithm.