Xian Li @xl_nlp
Research Scientist @AIatMeta FAIR. NLP, ML. Opinions are my own. California, USA Joined January 2019-
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(🧵) Today, we release Meta Code World Model (CWM), a 32-billion-parameter dense LLM that enables novel research on improving code generation through agentic reasoning and planning with world models. ai.meta.com/research/publi…
We released 44B synthetic tokens from our CoT-guided rewriting, offering higher quality pretraining data than the average human-written web texts📈 🤗Data: huggingface.co/datasets/faceb… 📜Paper: arxiv.org/abs/2506.04689 (accepted at #COLM2025) Excited to see what the community builds!
More and better data for general reasoning beyond math? NaturalThoughts outperforms OpenThoughts3, LIMO, S1k, etc. on GPQA-Diamond, SuperGPQA, and MMLU Pro. Download 1M+ reasoning prompts: huggingface.co/datasets/faceb…
More and better data for general reasoning beyond math? NaturalThoughts outperforms OpenThoughts3, LIMO, S1k, etc. on GPQA-Diamond, SuperGPQA, and MMLU Pro. Download 1M+ reasoning prompts: huggingface.co/datasets/faceb…
Love this project: nanoGPT -> recursive self-improvement benchmark. Good old nanoGPT keeps on giving and surprising :) - First I wrote it as a small little repo to teach people the basics of training GPTs. - Then it became a target and baseline for my port to direct C/CUDA…
Love this project: nanoGPT -> recursive self-improvement benchmark. Good old nanoGPT keeps on giving and surprising :) - First I wrote it as a small little repo to teach people the basics of training GPTs. - Then it became a target and baseline for my port to direct C/CUDA…
🌉 Bridging Offline & Online RL for LLMs 🌉 📝: arxiv.org/abs/2506.21495 New paper shows on verifiable & non-verifiable tasks: - Online DPO & GRPO give similar performance. - Semi-online (iterative) DPO with sync every s steps (more efficient!) works very well also. - Offline DPO…
The future of pretraining is synthetically organic ♻️♻️🌱🌱 We show that grounded synthetic data outperforms high-quality human-written web data such as curated DCLM.
The future of pretraining is synthetically organic ♻️♻️🌱🌱 We show that grounded synthetic data outperforms high-quality human-written web data such as curated DCLM.
Scaling RL is 🔥 what's the fuel? We propose a new paradigm to generate "synthetic environments", unlimited tasks with verifiable rewards for self-improvement. Welcome to the era of experience 🙌
Scaling RL is 🔥 what's the fuel? We propose a new paradigm to generate "synthetic environments", unlimited tasks with verifiable rewards for self-improvement. Welcome to the era of experience 🙌
This is exactly why we need to train the model specifically for human-agent collaborations. Check out a benchmark that we have made for “vibe coding” x.com/yifeizhou02/st…, existing models are still far from perfect and reasoning only seems to overfit more.
This is exactly why we need to train the model specifically for human-agent collaborations. Check out a benchmark that we have made for “vibe coding” x.com/yifeizhou02/st…, existing models are still far from perfect and reasoning only seems to overfit more.
How to improve LLMs for vibe coding? We released a benchmark and an RL algorithm!
As reasoning models are getting stronger, we need challenging and diverse questions which can elicit more thinking. Here're 1M+ reasoning questions beyond math and code to accelerate your work, whether you're building open R1 or distilling from strong reasoning models 🚀🚀🚀
As reasoning models are getting stronger, we need challenging and diverse questions which can elicit more thinking. Here're 1M+ reasoning questions beyond math and code to accelerate your work, whether you're building open R1 or distilling from strong reasoning models 🚀🚀🚀
"Pretraining as we know it will end". Enter CoCoMix 🥥🌪️We explored predicting concepts interleaving with next-token prediction, specifically predicting interpretable features from SAE. It demonstrates that steerability can be baked into LLM from the beginning!
"Pretraining as we know it will end". Enter CoCoMix 🥥🌪️We explored predicting concepts interleaving with next-token prediction, specifically predicting interpretable features from SAE. It demonstrates that steerability can be baked into LLM from the beginning!
💭🔎 Introducing EvalPlanner – a method to train a Thinking-LLM-as-a-Judge that learns to generate planning & reasoning CoTs for evaluation. Strong performance on RewardBench, RM-Bench, JudgeBench & FollowBenchEval. Paper 📄: arxiv.org/abs/2501.18099
We replicated the DeepSeek-R1-Zero and DeepSeek-R1 training on 7B model with only 8K examples, the results are surprisingly strong. 🚀 Starting from Qwen2.5-Math-7B (base model), we perform RL on it directly. No SFT, no reward model, just 8K MATH examples for verification, the…
Our Coconut work (learning continuous latent CoT) has opened sourced now. Welcome to play with it: github.com/facebookresear…
New paradigm of reasoning 🥥🥥🥥 brilliant work led by @Ber18791531
New paradigm of reasoning 🥥🥥🥥 brilliant work led by @Ber18791531
Open science is how we continue to push technology forward and today at Meta FAIR we’re sharing eight new AI research artifacts including new models, datasets and code to inspire innovation in the community. More in the video from @jpineau1. This work is another important step…
We are hiring 2025 research interns at FAIR. Topics of interest include LLM reasoning, planning, alignment, synthetic data, efficient architecture and training algorithms, etc. Apply at metacareers.com/jobs/532549086…
Strong models for llm as a judge released from Jason's team and Meta. Really excited to see how far we can bring this capability in open models.
Strong models for llm as a judge released from Jason's team and Meta. Really excited to see how far we can bring this capability in open models.
Releasing model, data, and code for LLM judge!
While o1 is impressive, for math problems that require out-of-box thinking, o1 still does poorly. Surprised to see o1-preview cannot even detect some obvious errors in its logic chain. See below for a few examples. Maybe o1 (from what it shows in o1-preview) assumes "there is…

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