Generalization Dynamics of LM Pre-Training
The article discusses the generalization dynamics of language models (LMs) during pre-training, revealing a phenomenon called mode-hopping. This behavior shows that LMs can abruptly switch between parrot-like and intelligence-like modes, challenging the traditional view of gradual learning. The findings suggest that generalization is influenced by competition between different circuits within the model, and scaling alone does not eliminate mode-hopping.
- ▪The eval suite developed tracks generalization dynamics across LM pre-training.
- ▪Mode-hopping occurs frequently, with LMs switching between different operational modes during training.
- ▪Scaling parameters can help mitigate competition between circuits, but do not completely resolve mode-hopping.
Hacker News (Newest) files mainly under programming. We currently carry 5,306 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://jiaxin-wen.github.io/blog/generalization-dynamics |
| Publication time | Wed, 20 May 2026 02:10:24 +0000 |
| Retrieval time | 2026-05-20T02:34:58.982Z |
| Last seen | 2026-05-20T02:34:58.982Z |
| 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 | tPBN6Vs75OuB |
| 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
We build an eval suite that exposes such behavioral fingerprints for generalization (see Table 1 for details), and use it to track generalization dynamics across LM pre-training. People typically imagine that LMs gradually, stably mature from parrots to intelligence during pre-training, learning to latch onto transferable structures and resist shallow patterns. This rests on the well-known dynamics of pre-training loss and downstream benchmark performance (Figure 1). We find this mental model is wrong: throughout pre-training, LMs frequently and suddenly hop between parrot-like and intelligence-like modes, i.e. distinct algorithms implemented by distinct circuits. We call this mode-hopping.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Github.
Discussion
0 commentsMore from Github
