Show HN: Minimal LLM Post-Training Experiments on an 8GB GPU (SFT, DPO, GRPO)
TL;DR Built on HuggingFace TRL, in under 100 lines of core code you can run the whole SFT + DPO + GRPO post-training pipeline end-to-end on a single 8GB GPU with a tiny 0.14B (135M) model. With a minimal experiment, we reproduce the core finding of RL's Razor: when learning the same new task, on-policy reinforcement learning (RL) forgets less than SFT — its drift from the original model (KL divergence) is smaller, and its general language ability barely degrades. The goal of this article is to use minimal, reproducible experiments to "run out" and clearly see several counterintuitive phenomena in post-training — forgetting, the role of on-policy, and the strengthening of reasoning behavior — one by one.
- ▪TL;DR Built on HuggingFace TRL, in under 100 lines of core code you can run the whole SFT + DPO + GRPO post-training pipeline end-to-end on a single 8GB GPU with a tiny 0.14B (135M) model.
- ▪With a minimal experiment, we reproduce the core finding of RL's Razor: when learning the same new task, on-policy reinforcement learning (RL) forgets less than SFT — its drift from the original model (KL divergence) is smaller, and its gen
- ▪The goal of this article is to use minimal, reproducible experiments to "run out" and clearly see several counterintuitive phenomena in post-training — forgetting, the role of on-policy, and the strengthening of reasoning behavior — one by
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,176 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/pochenai/nano-llm-posttraining |
| Publication time | Sat, 01 Aug 2026 12:30:32 +0000 |
| Retrieval time | 2026-08-01T13:03:30.204Z |
| Last seen | 2026-08-01T13:03:30.204Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | KSx315pHFerZ · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Minimal LLM Post-Training on an 8GB GPU: Understanding KL, SFT, DPO, GRPO and DeepSeek-Style Reasoning with Open-Source Frameworks Using open-source training frameworks (HuggingFace TRL) and minimal, reproducible experiments to see — one by one — what SFT, DPO and GRPO each change, how RL drifts less than SFT (measured by KL), and how GRPO amplifies DeepSeek-R1-style reasoning. TL;DR Built on HuggingFace TRL, in under 100 lines of core code you can run the whole SFT + DPO + GRPO post-training pipeline end-to-end on a single 8GB GPU with a tiny 0.14B (135M) model.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.