Researcher @ OpenAI | PhD from Princeton
Joined September 2018
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Dingli Yu retweeted
Today we are launching Muse Wonder and Muse Wonder Full, our best models yet, with state-of-the-art performance on personal and professional agentic tasks. They are built for long running, multi step work. Muse Wonder is available today in the Meta Model API and Muse Code, and Muse Wonder Full in Muse Code.
Dingli Yu retweeted
introducing muse wonder
our new frontier model. state-of-the-art at personal agents and professional agents.
our largest and most capable model yet—rivals the big bois
Dingli Yu retweeted
1/ today we’re releasing muse spark 1.3—available in muse code & the meta model api.
this is our most capable model yet—frontier performance almost too cheap to meter. much stronger at agentic and coding with better usability. we think users will really notice the jump.
Dingli Yu retweeted
Muse Spark 1.3 is rolling out today with frontier performance almost too cheap to meter. This is the biggest jump we've made so far on coding and agentic work. Try it in Muse Code and our API.
Next up 🍉 and Muse Spark open weights releases coming soon.
Dingli Yu retweeted
Today we're releasing Muse Spark 1.3, our strongest model for agentic and coding tasks in the spark model line. It’s designed to support longer-horizon work, excel in agentic tasks, and follow complex instructions more reliably than previous models. Muse Spark 1.3 is available today in Muse Code and Meta Model API.
research.meta.ai/blog/introd…
Dingli Yu retweeted
(1) Today we're releasing Muse Spark 1.1 -- a strong agentic and coding model at a very low price. It's available through our new Meta Model API and in Meta AI.
Dingli Yu retweeted
1/ muse spark 1.1 is an industry-competitive agentic and coding model. across many agentic evals it rivals gpt-5.5 and opus-4.8.
available now through the new meta model api and in meta ai. 🧵
Dingli Yu retweeted
Today we are launching Muse Spark 1.1, an upgrade to muse spark 1 that greatly improves agentic, coding, multimodal, and computer use capabilities. We're also launching the Meta Model API in public preview.
ai.meta.com/blog/introducing…
Dingli Yu retweeted
Meta is back! Muse Spark scores 52 on the Artificial Analysis Intelligence Index, behind only Gemini 3.1 Pro, GPT-5.4, and Claude Opus 4.6. Muse Spark is the first new release since Llama 4 in April 2025 and also Meta's first release that is not open weights
Muse Spark is a new model from @Meta evaluated on Artificial Analysis. We were given early access by Meta to independently benchmark the model. It is the first frontier-class model from Meta since Llama 4 Maverick was released in April 2025, and notably the first @AIatMeta model that is not being released as open weights. The release follows Meta's reorganization of its AI efforts under Meta Superintelligence Labs, and signals that Meta is re-entering the frontier race after roughly a year of relative quiet.
For context, Llama 4 Maverick and Scout scored 18 and 13 respectively on the Artificial Analysis Intelligence Index as non-reasoning models at the time of their release, while Muse Spark scores 52. Muse Spark essentially closes the gap between to the frontier in a single release.
The model is not open source and is not yet accessible via an API but Meta has shared they expect this to come soon. Meta is also integrating Muse Spark into their first party products including their Meta AI chat product, Facebook, Instagram and Threads.
Key takeaways from our benchmarks:
➤ Muse Spark scores 52 on the Artificial Analysis Intelligence Index, placing it within the top 5 models we have benchmarked. It sits ahead of Claude Sonnet 4.6, GLM-5.1, MiniMax-M2.7, Grok 4.20 and behind Gemini 3.1 Pro Preview, GPT-5.4 and Claude Opus 4.6
➤ Muse Spark is notably token efficient for its intelligence level. It used 58M output tokens to run the Intelligence Index, comparable to Gemini 3.1 Pro Preview (57M) and notably lower than Claude Opus 4.6 (Adaptive Reasoning, max effort, 157M), GPT-5.4 (xhigh, 120M) and GLM-5 (110M)
➤ Muse Spark is the second-most capable vision model we have benchmarked. It scores 80.5% on MMMU-Pro, behind only Gemini 3.1 Pro Preview (82.4%)
➤ Muse Spark performs strongly on reasoning and instruction-following evaluations. It scores 39.9% on HLE, trailing only Gemini 3.1 Pro Preview (44.7%) and GPT-5.4 (xhigh, 41.6%). The model also achieved 5th highest in CritPT with a score of 11%, an eval that is focused on difficult physics research questions. This is substantially above above Gemini 3 Flash (9%) and Claude 4.6 Sonnet (3%)
➤ Agentic performance does not stand out. On GDPval-AA, our evalaution focused on real world work tasks, Muse Spark scores 1427, behind both Claude Sonnet 4.6 at 1648 and GPT-5.4 at 1676, but ahead of Gemini 3.1 Pro Preview at 1320. On On TerminalBench Hard, Muse Spark trails Claude Sonnet 4.6, GPT-5.4, and Gemini 3.1 Pro. Muse Spark joins others in achieving a high τ²-Bench Telecom score of 92%
Key model details:
➤ Modalities: Multimodal including text and vision input, text output
➤ License: Proprietary, Meta's first frontier model not released as open weights
➤ Availability: No public API at the time of publishing. Meta expects to provide API access soon. Meta has started integration into their first party AI offering Meta AI and inside Facebook, Instagram, and Threads
Dingli Yu retweeted
Check out Muse Spark, our first milestone in the quest for personal superintelligence! Scaling this with the team has been a total blast. Give it a spin and let us know what you think! 🥑
Dingli Yu retweeted
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. 🧵
Dingli Yu retweeted
Excited to share what we’ve been building at Meta Superintelligence Labs! We just released Muse Spark, our first AI model. It's a natively multimodal reasoning model and the first step on our path to personal superintelligence. We've overhauled our entire stack to support scaling, and this is just the beginning.
ai.meta.com/blog/introducing…
Dingli Yu retweeted
Here at @OpenAI we've cracked pretraining, then reasoning, and now we're experimenting with a new set of techniques that maximally leverage their interaction. GPT-5 is just the first step in this direction, and we're incredibly excited to see where scaling this up will lead us!
Dingli Yu retweeted
Does all LLM reasoning transfer to VLM? In context of Simple-to-Hard generalization we show: NO! We also give ways to reduce this modality imbalance.
Paper arxiv.org/abs/2501.02669
Code github.com/princeton-pli/VLM…
@Abhishek_034 @chengyun01 @dingli_yu @anirudhg9119 @prfsanjeevarora
Dingli Yu retweeted
Excited to train o3-mini with @ren_hongyu @_kevinlu and others, a blindingly fast model with amazing reasoning / code / math performance.
openai.com/12-days/?day=12
Dingli Yu retweeted
@QuantaMagazine featured our work on emergence of skill compositionality (and its limitations) in LLMs among the CS breakthroughs of the year. tinyurl.com/5f5jvzy5. Work was done over 2023 @GoogleDeepMind and @PrincetonPLI. Key pieces: (i) mathematical framework for quantifying how LLM scaling leads to predictable increase in the model’s ability to combine skills while solving new tasks. Joint work with @anirudhg9119 (ii) experiments verifying theoretical prediction in experiments via SkillMix evaluation (lead author @dingliy_yu) (iii) the level of skill-compositionality detected in GPT4O via Sept'23 experiments mathematically imply that it is able to reason and talk about situations it has not seen in its training data —i.e. it has moved beyond the “stochastic parrots” stereotype that had dogged earlier LLMs.
Skill emergence paper: arxiv.org/abs/2307.15936
Skillmix Evaluation: arxiv.org/abs/2310.17567
Models can improve skill composition from examples arxiv.org/abs/2409.19808
Wonderful to work with the colleagues and students involved.
Dingli Yu retweeted
Surprise #NeurIPS2024 drop for y'all: phi-4 available open weights and with amazing results!!!
Tl;dr: phi-4 is in Llama 3.3-70B category (win some lose some) with 5x fewer parameters, and notably outperforms on pure reasoning like GPQA (56%) and MATH (80%).
Dingli Yu retweeted
🚀 Phi-4 is here! A small language model that performs as well as (and often better than) large models on certain types of complex reasoning tasks such as math. Useful for us in @MSFTResearch, and available now for all researcher on the Azure AI Foundry! aka.ms/phi4blog
Safer practice for tuning chatbots: fine-tune without the safety prompt and inference with it!
Works surprisingly well in practical settings — fine-tuning on benign dataset to improve downstream tasks while keeping it safe.
Fine-tuning can improve chatbots (e.g., Llama 2-Chat, GPT-3.5) on downstream tasks — but may unintentionally break their safety alignment.
Our new paper: Adding a safety prompt is enough to largely mitigate the issue, but be cautious about when to add it! arxiv.org/abs/2402.18540
Dingli Yu retweeted
Launching blog @PrincetonPLI with a post on skillmix. LLMs aren't just "stochastic parrots." @geoffreyhinton recently mentioned this as evidence that LLMs do "understand" the world a fair bit. More blog posts on the way! (Hinton's post here: teapawt.net/geoffreyhinton/status/…)
We are excited to introduce the PLI Blog! pli.princeton.edu/blog
First post by @prfsanjeevarora, "Are Language Models Mere Stochastic Parrots? The SkillMix Test Says NO." bit.ly/47PpKp4
ALT Parrot illustration