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Dat Huynh
@DatHuynh13
Research Scientist at Meta
Boston
hbdat.github.io
Joined May 2016
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    Dat Huynh
    @DatHuynh13
    Jul 10
    🥳
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    Mark Zuckerberg
    Meta
    @finkd
    Jul 9
    Replying to @finkd
    (2) Muse Spark 1.1 is strongest at agentic performance, tool use, and computer use. It does well on long-running tasks with 1M token context window, can delegate execution to sub-agents running in parallel, and is trained to use computer interfaces on desktop, mobile, or browser.
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    Dat Huynh
    @DatHuynh13
    Nov 7, 2025
    Agentic behavior will be the key to Super Intelligence, acting over long horizons to solve extremely complex problems. 🚀 We’re pleased to introduce a scalable synthetic environment that enables rich, diverse, and reward-dense RL training — ushering in the new age of experience.
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    Jason Weston
    @jaseweston
    Nov 7, 2025
    Scaling Agent Learning via Experience Synthesis 📝: arxiv.org/abs/2511.03773 Scaling training environments for RL by simulating them with reasoning LLMs! Environment models + Replay-buffer + New tasks = cheap RL for any environments! - Strong improvements over non-RL-ready
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    Dat Huynh
    @DatHuynh13
    Oct 16, 2025
    Curious about how to improve agent performance? Check out Kai Zhang work on "early experience" training 😊
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    WebAgentlab
    @webagentlab
    Oct 13, 2025
    Replying to @webagentlab
    Agent Learning via Early Experience The paper introduces the “early experience” paradigm for training autonomous language agents, which enables them to generate their own interaction data for improved learning and generalization, effectively bridging imitation and reinforcement
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    Dat Huynh
    @DatHuynh13
    Sep 26, 2025
    This is so cool! Open-source replication of Genie 3 world model
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    anandmaj
    @Almondgodd
    Sep 25, 2025
    I spent the past month reimplementing DeepMind’s Genie 3 world model from scratch Ended up making TinyWorlds, a 3M parameter world model capable of generating playable game environments demo below + everything I learned in thread (full repo at the end)👇🏼
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