DPO

Answer-first: For 80% of domain enterprise tasks, fine-tuned Small Language Models (1B–14B) match frontier performance at 1/50th inference cost and sub-40ms latency. This playbook provides the production engineering blueprint: synthetic data curation, QLoRA fine-tuning with Axolotl on 24GB GPUs, DeepSeek-R1 reasoning distillation, DPO/GRPO alignment, and vLLM continuous batching deployment. 🇻🇳 Read the Vietnamese version of this series on 📖 Bản tiếng Việt (Vietnamese Edition) 🎯 Series Overview: Why Small Language Models in 2026? Relying exclusively on proprietary frontier API models (GPT-4.5, Claude 3.5 Sonnet) introduces three fatal enterprise vulnerabilities: ...

← Previous Chapter: Part 4: Knowledge Distillation | Series Hub | Next Chapter: Part 6: vLLM Deployment & Automated Evals → Prerequisite: Read Part 4: Knowledge Distillation from DeepSeek-R1 & Frontier Teachers for Chain-of-Thought reasoning distillation. Answer-first: Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO) supersede unstable 4-model PPO pipelines for SLM alignment. By deriving implicit rewards directly from reference model log-probabilities or computing group-relative advantages without Critic networks, developers enforce 99.8% JSON schema compliance and eliminate hallucinations on single 24GB GPUs with zero reinforcement learning instability. ...