@micmylin profile picture
emily mcmilin @micmylin

RL and world models for coding at FAIR

Joined December 2008
  • Tweets 156
  • Following 647
  • Followers 684
42 Photos and videos
emily mcmilin retweeted
Kilian Lieret@KLieret
May 28
Very interesting study from Opus 4.8 card: Multi-agents do not deliver better results on ProgramBench, but they get to mediocre solutions 2x faster.
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emily mcmilin retweeted
John Yang
@jyangballin
May 5
How much of SQLite, FFmpeg, PHP compiler can LMs code from scratch? Given just an executable and no starter code or internet access. Introducing ProgramBench: 200 rigorous, whole-repo generation tasks where models design, build, and ship a working program end to end. šŸ§µ
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emily mcmilin retweeted
Yuxiang Wei
@YuxiangWei9
Apr 30
Accepted to ICML 2026! Big thanks to all the collaborators šŸŽ‰
Yuxiang Wei
@YuxiangWei9
23 Dec 2025
Software agents can self-improve via self-play RL Introducing Self-play SWE-RL (SSR): training a single LLM agent to self-play between bug-injection and bug-repair, grounded in real-world repositories, no human-labeled issues or tests. šŸ§µ
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I'll be giving a talk at the ICLR VerifAI workshop, about code execution for code world modeling, later today (Sun) at 9:05 am (Brazil time). Swing by if you are interested in learning more!
Ameesh Shah@ameeshsh
Jan 12
šŸ—£ļøšŸ“£Announcing VerifAI 2: AI Verification in the Wild, an upcoming workshop at #ICLR2026!! šŸ—£ļøšŸ“£ VerifAI will gather researchers to explore topics at the intersection of genAI and trustworthy ML. Submit your work! Check out our website and CFP for more: verifai-workshop.github.io/
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emily mcmilin retweeted
Zhiqing Sun
@EdwardSun0909
Apr 8
Excited to share Muse Spark, the first model from whole teamā€™s work in MSL! šŸš€ Itā€™s natively multimodal and agentic. Iā€™ve been using it for my daily coding and research tasks. Still plenty of room to improve in agentic domains, but weā€™re moving with great velocity. Itā€™s a seriously good model! Check out the full breakdown and try it out in meta.ai

Meta AI

Use Meta AI assistant to get things done, create AI-generated images for free, and get answers to any of your questions.

meta.ai
Alexandr Wang
@alexandr_wang
Apr 8
1/ today we're releasing muse spark, the first model from MSL. nine months ago we rebuilt our ai stack from scratch. new infrastructure, new architecture, new data pipelines. muse spark is the result of that work, and now it powers meta ai. šŸ§µ
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emily mcmilin retweeted
Yuxiang Wei
@YuxiangWei9
23 Dec 2025
Software agents can self-improve via self-play RL Introducing Self-play SWE-RL (SSR): training a single LLM agent to self-play between bug-injection and bug-repair, grounded in real-world repositories, no human-labeled issues or tests. šŸ§µ
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Better late than never to share how we built 35k unique repos (rather than commits from the same dozens of repos) into executable envs for CWM mid-training and SWE-RL post-training... x.com/syhw/status/1970960837ā€¦

Gabriel Synnaeve@syhw
24 Sep 2025
(šŸ§µ) 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/publicaā€¦
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Key insight: the execution env of a GitHub Actions CI workflow is fully built with deps. So we can cheaply capture it as a standalone Docker image for later execution.
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We modify each repo's CI workflows to capture a single successful third-party build. For pytest repos, we inject conftest.py fixtures to verify the correct container and support optional Python execution tracing. See more in our paper: arxiv.org/abs/2510.02387

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emily mcmilin retweeted
Taco Cohen
@TacoCohen
4 Sep 2025
The eagle-eyed goat in question being @YuxiangWei9
Lucas Beyer (bl16)
@giffmana
3 Sep 2025
Goated FAIR team just found how coding agents sometimes "cheat" on SWE-Bench Verified. It's really simple. For example, Qwen3 literally greps all commit logs for the issue number of the issue it needs to fix. lol, clever model. "cheat" cuz it's more like env hacking.
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Thank you @AleksanderMolak for the really nice opportunity to discuss some of my prior research with you, earlier this year! x.com/aleksandermolak/statusā€¦

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Link to video where our part of the convo starts: youtube.com/watch?v=sljBU_HFā€¦ Botched last attempt to send this. But better late than never...

Causal Bandits at AAAI 2024 | Part 1 | CausalBanditsPodcast.com

*Causal Bandits at AAAI 2024 || Part 1**In this special episode w...

youtube.com
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Dreams can come true. Iā€™ve joined FAIRā€™s CodeGen team. :)
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emily mcmilin retweeted
Udacity
@udacity
28 Apr 2024
šŸ’” Interested in learning more about LLM fundamentals? In the video below, Udacity instructor Emily McMilin explains what the Transformer model is & walks you through the difference between Encoder and Decoder model architectures. bit.ly/44f0eJn #genAI #generativeAI
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Our research showing how task underspecification can cause spurious correlations & hallucinations, from BERT to GPT-3.5 is now available as AAAI 24 proceedings: ojs.aaai.org/index.php/AAAI/ā€¦ Video: underline.io/lecture/92119-uā€¦ Arxiv extended to GPT-4 Turbo Preview: arxiv.org/abs/2210.00131
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Full house at the Causal Parrots workshop at #AAAI24 llmcp.cause-lab.net/llmcp
Every seat taken and audience overflowing

ALT Every seat taken and audience overflowing

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Thanks to all who stopped by my poster last night @RealAAAI. If you are interested in talking more about causality and LLMs here at #AAAI24 or beyond, please reach out!
Poster presented in AAAI 2024 Main Track for paper here: https://arxiv.org/abs/2210.00131

ALT Poster presented in AAAI 2024 Main Track for paper here: https://arxiv.org/abs/2210.00131

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Scaling up (to GPT-4 Turbo Preview) doesnā€™t help fix specification-induced spurious correlations. With access to GPT-4ā€™s logprobs, we subjected it to the same methods that had found these spurious correlations in models from BERT-base to GPT-3.5. /1 x.com/micmylin/status/173362ā€¦

emily mcmilin@micmylin
9 Dec 2023
Now accepted at #AAAI24. I started single author, indie research as a ~jr SWE, recently transitioned from hardware engineering. Learned so much along the way, with help from MLC's @savvyRL & @jasonyo, Cohere4AI's @oohaijen & @sarahookr, HF's @SashaMTL and anon peers. Grateful.
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Good news: As was the case with smaller models, with GPT-4 Turbo Preview, we can exploit these spurious correlations to separate well-specified from unspecified tasks, with just one extra inference pass. 3/
Task Specification Metric results from GPT-4 Turbo Preview on the Winogender- Simplified benchmark. This method exploits our finding that well-specified texts are less likely to exhibit specification-induced spurious correlations. ā€˜Well-specifiedā€™ texts are demarked with a blue horizontal or vertical bar. The remaining texts have a ground truth label of ā€˜unspecifiedā€™. Perfect detection would appear as a horizontal row of blue ā€˜plusā€™ symbols (composed of the markers from both well-specified texts) below some thresholding line, with all the green markers above.

ALT Task Specification Metric results from GPT-4 Turbo Preview on the Winogender- Simplified benchmark. This method exploits our finding that well-specified texts are less likely to exhibit specification-induced spurious correlations. ā€˜Well-specifiedā€™ texts are demarked with a blue horizontal or vertical bar. The remaining texts have a ground truth label of ā€˜unspecifiedā€™. Perfect detection would appear as a horizontal row of blue ā€˜plusā€™ symbols (composed of the markers from both well-specified texts) below some thresholding line, with all the green markers above.

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Check out the updated paper, and if you're at AAAI, check out my poster, Main Track, Friday, 7-9p arxiv.org/abs/2210.00131 done/
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