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Description
AutoScientist is an innovative system designed to enhance and automate the comprehensive research process involved in model training and alignment, empowering more teams to influence and improve the AI technologies they rely on. Although model training and reinforcement learning serve as some of the most effective methods for model development, achieving success in these areas can be particularly challenging outside of leading research facilities due to issues like catastrophic forgetting, overfitting on limited or subpar datasets, and conflicting training signals. AutoScientist automatically co-optimizes both data and model training strategies, continuously refining both aspects until the outcome aligns with the user’s objectives. While Adaptive Data focuses on optimizing inputs, AutoScientist is dedicated to refining the model, effectively executing the entire research cycle from start to finish, ensuring users receive models that are finely tuned to their specific goals. This self-sustaining process allows for simultaneous co-optimization of data and training strategies, iterating seamlessly until the model achieves the desired behavior as specified by the user, ultimately leading to enhanced performance and usability.
Description
Muse Spark 1.2 is Meta’s newest coding-focused model, released alongside Muse Code as part of Meta’s AI developer platform. The model improves on Muse Spark 1.1 with stronger code generation, complex debugging, codebase understanding, and full developer workflow performance. Muse Spark 1.2 powers Muse Code, a terminal coding agent that can plan changes, write code, validate results, and coordinate persistent background subagents. The model was co-trained with Muse Code so it performs well inside the agentic coding runtime and tool environment. Its training included scaled coding compute, broader training environment diversity, rejection-sampled harness trajectories, recipe optimizations, and Muse Code toolset integration. Muse Spark 1.2 is designed for long-horizon coding tasks such as whole-repository generation, large end-to-end projects, auto-research, and extended optimization work. It uses planning to sequence work, goal conditioning to stay aligned with the user’s objective, and context compaction to preserve useful knowledge over long sessions. The model also benefits from a self-improvement loop where Muse Spark 1.1 generated challenging coding environments and instruction-following templates for training. By combining coding specialization, agentic workflow support, long-horizon training, subagent compatibility, and Meta Model API availability, Muse Spark 1.2 helps developers build, debug, and optimize software more effectively.
API Access
Has API
API Access
Has API
Integrations
.NET
C#
CSS
Claude Code
Go
Hermes Agent
Instagram
LlamaIndex
Lua
Model Context Protocol (MCP)
Integrations
.NET
C#
CSS
Claude Code
Go
Hermes Agent
Instagram
LlamaIndex
Lua
Model Context Protocol (MCP)
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
$1.25 per 1M tokens (input)
$1.25 per million tokens in input, and $4.25 per million tokens of output
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
AutoScientist
Country
United States
Website
www.adaptionlabs.ai/blog/autoscientist
Vendor Details
Company Name
Meta
Founded
2004
Country
United States
Website
meta.ai
