LLM Fine-Tuning and RL Tutorial
leslysandra · x · 2026-07-11
This post shares an in-depth guide on how to fine-tune LLMs in 2026. The core idea is that when system prompts, few-shot examples, and temperature adjustments are no longer enough to meet task requirements reliably, it's time to shift to systematic fine-tuning.
The article lists common and advanced techniques, including LoRA, QLoRA, Prefix/Adapter/P-Tuning, Instruction Tuning, BitFit, Soft Prompts, RLHF, RLAIF, DPO, GRPO, Multi-Task Fine-Tuning, and Federated Fine-Tuning. It emphasizes fine-tuning LLMs using RL and mentions using automated, LLM-graded rewards to reduce the manual effort required for reward engineering. The author notes that this entire pipeline is built on 100% open-source tools.
More from Research
- OpenAI says long-horizon models need safety and alignment checks across full action sequences — rhiever · 2026-07-22
- A Reddit user proposes a consistency LoRA to keep anime and game scenes visually stable — ThirdWorldBoy21 · 2026-07-22
- Graph workload 854.graph500 enters SPEC CPU 2026 as a new CPU benchmark — Prof_DavidBader · 2026-07-22
- BlackboxNLP 2026 is recruiting extra reviewers after a high submission volume — hanjie_chen · 2026-07-22
- AWS shows self-distilled reasoning can preserve math and coding skills during SFT — AWS ML Blog · 2026-07-22
- UI2App shows screenshot fidelity still lags real interaction recovery — Grace Man Chen · 2026-07-22