Dive in 📑: arxiv.org/abs/2506.16029

Blog Post 📝: zhentingqi.github.io/internal/pro...

Thread 🧵: x.com/_hanlin_zhan...

Work by Zhenting Qi, and the team Fan Nie, Alexandre Alahi, @jameszou.bsky.social, Himabindu Lakkaraju, Yilun Du, Eric Xing, @shamkakade.bsky.social

✅ Open-source everything — models, data, training, and evaluation pipeline

✅ Maintain the EvoLM model family with clear data provenance

✅ Support the community in extending this foundation for future LLM research

We seek to:

✅ Build a fully transparent and reproducible model suite for studying LM training

✅ Quantify how each training phase contributes to upstream cloze task performance and downstream generative task performance, considering both in-domain and out-of-domain settings

Introducing EvoLM, a model suite with 100+ decoder-only LMs (1B/4B) trained from scratch, across four training stages —

🟦 Pre-training
🟩 Continued Pre-Training (CPT)
🟨 Supervised Fine-Tuning (SFT)
🟥 Reinforcement Learning (RL)

New work [JSKZ25] w/ Jikai, Vasilis,
@shamkakade.bsky.social .

We introduce new formulations and tools for evaluating LM capabilities, which help explain observations of post-training behaviors of Qwen-series models.

More details:

- hanlin-zhang.com/causal-capab...
- x.com/_hanlin_zhan...

[4/4] Prompt injection can extract private datastore content—verbatim—from RAG:

– Black-box attack can leak 41% of a book with just 100 queries
– Vulnerability grows with model size and instruction tuning
– Mitigation: eliminate position bias (via PINE)+system prompts

(arxiv.org/abs/2402.17840)

[3/4] LMs can suffer from position bias—they favor content based on where it appears. This can hurt reasoning and evaluation.
We introduce PINE, a training-free method that eliminates position bias via bidirectional attention+reordering docs by attention scores.
(arxiv.org/abs/2407.01100)

[2/4] Can LLMs self-improve by verifying their own outputs? This paper says yes—with a twist. The key lies in a measure: the Generation-Verification Gap (GV-Gap) that scales with pretraining FLOPs in a log-linear trend.
Oral @yus167.bsky.social 6A: Sat 26 Apr 4:18-4:30.
(arxiv.org/abs/2412.02674)

[1/4]
This work:
- Shows that CBS scales with data size, not model size
- Provides theory + empirical scaling laws
- Suggests more data → higher CBS → more efficient data-parallel
Learn more: x.com/_hanlin_zhan...
Poster at Hall 3 #376, Thu 24 Apr 10-12:30.

[1/4] Modern large-scale LM training is limited not just by compute, but by data movement—a classic Von Neumann bottleneck (research.ibm.com/blog/why-von...).

Scaling batch size reduces optimization steps, but only up to a point—the Critical Batch Size (CBS).

Highlights from #ICLR2025 — a brief thread 🧵

I want to reshare @brandfonbrener.bsky.social's @NeurIPSConf 2024 paper on CoLoR-Filter: A simple yet powerful method for selecting high-quality data for language model pre-training!

With @hlzhang109.bsky.social @schwarzjn.bsky.social @shamkakade.bsky.social

(1/n) 💡How can we speed up the serial runtime of long pre-training runs? Enter Critical Batch Size (CBS): the tipping point where the gains of data parallelism balance with diminishing efficiency. Doubling batch size halves the optimization steps—until we hit CBS, beyond which returns diminish.

LLM self-improvement has critical implications in synthetic data, post-training and test-time inference. To understand LLMs' true capability of self-improvement, we perform large-scale experiments with multiple families of LLMs, tasks and mechanisms. Here is what we found: (1/9)