I RL-finetuned an LLM to unslop my writing
A weekend project involved fine‑tuning a 4‑billion‑parameter language model using reinforcement learning to rewrite text and reduce AI‑detector scores. The author created synthetic “AI slop” data, used rule‑based and neural detectors as reward signals, and added content‑ and tone‑preservation judges to maintain meaning. The resulting model performs well against many open‑source detectors but struggles with closed‑source ones, indicating limited generalisation.
- ▪The author generated a synthetic dataset where one LLM produced scenarios and another fleshed out the text to create examples of AI‑sloppy writing.
- ▪Initial training used a rule‑based detector called slop‑guard, which caused the model to collapse into overly short sentences, prompting the addition of a content‑preservation reward via an LLM judge.
- ▪Subsequent experiments incorporated fine‑tuned BERT detectors and tone‑preservation rewards to balance formality and meaning.
- ▪When aggregating multiple open‑source detectors as rewards, the model achieved strong performance on those detectors but showed weaker results on closed‑source detectors like Pangram.
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Record
| Original publisher | castform |
| Canonical URL | https://castform.com/blog/unslop/ |
| Publication time | Tue, 04 Aug 2026 10:10:47 +0000 |
| Retrieval time | 2026-08-04T10:15:41.398Z |
| Last seen | 2026-08-04T10:15:41.398Z |
| 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 | Ta0ZcYl7tPek · 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
tl;dr: as a weekend project, i trained a 4b model with reinforcement learning to rewrite & “unslop” text to beat ai detectors. i got claude to generate this full blogpost and then got the above finetuned model to rewrite it. here’s the training run. ai slop i have been doing a lot of writing recently and like many people, ai models are a regular part of my workflow. it’s useful to get past the blank page block & flesh out disconnected ideas. the problem though is that the writing sounds like ai. it is not necessarily bad writing. it is just generic writing. commonly referred to as ai slop.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at castform.
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