Muse Spark 1.2Meta
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Related Products
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About
AutoScientist is a system that self-improves and automates the full research loop behind model training and alignment, making it possible for more teams to shape and refine the AI they depend on. Model training and reinforcement learning are among the most powerful ways to shape a model, but they are also among the hardest to get right outside a frontier lab because attempts can fail through catastrophic forgetting, overfitting on small or low-quality datasets, and conflicting training signals. AutoScientist co-optimizes data and model training recipes automatically, self-improving across both until quality converges on the user’s objective. Where Adaptive Data shapes the inputs, AutoScientist shapes the model, running the full research loop end-to-end so users walk away with models adapted to their goal. The loop runs itself: data and recipes are co-optimized in lockstep, iterating until the model converges on the behavior described.
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About
Muse Spark 1.2 is Meta’s coding-focused model update designed to power Muse Code and improve software engineering workflows. The model is built for code generation, complex debugging, codebase understanding, long-horizon development tasks, and end-to-end developer workflows. Muse Spark 1.2 was co-trained with Muse Code to improve performance inside the terminal coding agent environment. It supports planning, goal conditioning, context compaction, subagent coordination, and iterative coding workflows across large repositories. The model was trained with expanded coding compute, diverse development environments, self-improvement loops, and long-running engineering tasks. Built for AI developers and software teams, Muse Spark 1.2 helps agents plan, write, validate, debug, and optimize code with greater autonomy.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
AI builders and enterprise teams that need to train, adapt, and own models without manually managing complex research loops
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Audience
AI developers, software engineers, coding agent builders, research teams, platform teams, DevOps teams, ML engineers, enterprise development teams, and organizations that need code generation, debugging, codebase understanding, long-horizon coding, terminal agents, subagent coordination, repository automation, kernel optimization, and end-to-end developer workflow support
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
No information available.
Free Version
Free Trial
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Pricing
$1.25 per 1M tokens (input)
$1.25 per million tokens in input, and $4.25 per million tokens of output
Free Version
Free Trial
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Reviews/
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Reviews/
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Pros & Cons from Real UsersPros
Cons
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationAutoScientist
United States
www.adaptionlabs.ai/blog/autoscientist
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Company InformationMeta
Founded: 2004
United States
meta.ai
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Categories |
Categories |
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Integrations
Claude Code
Facebook Messenger
Go
HTML
Instagram
Kotlin
Kubernetes
LlamaIndex
Lua
Meta AI
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Integrations
Claude Code
Facebook Messenger
Go
HTML
Instagram
Kotlin
Kubernetes
LlamaIndex
Lua
Meta AI
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