Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

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

Ornith-1.0 represents an innovative family of models tailored specifically for coding tasks that require agentic capabilities. This family encompasses a wide range of models, from the compact 9B Dense versions ideal for deployment on edge devices to the expansive 397B MoE frontier-scale models designed for peak performance, including variants such as 9B Dense, 31B Dense, 35B MoE, and 397B MoE. Built upon the foundational strengths of pretrained models like Gemma 4 and Qwen 3.5, Ornith-1.0 excels in achieving top-tier performance among open-source models that are similar in size when evaluated against coding benchmarks. A significant breakthrough of this model is its self-improving training framework, which effectively learns to produce both solution rollouts and the tailored scaffolds that direct those rollouts. Rather than depending on static, human-crafted harnesses, Ornith-1.0 perceives the scaffold as a dynamic entity that evolves alongside the policy, enabling the model to optimize both the orchestration of tasks and the resulting solutions in tandem. This dual optimization approach enhances the model's adaptability and effectiveness in real-world coding scenarios.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
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

DeepReinforce

Country

United States

Website

deep-reinforce.com/ornith_1_0.html

Product Features

Product Features

Alternatives

Tinker Reviews

Tinker

Thinking Machines Lab

Alternatives

Claude Opus 4.7 Reviews

Claude Opus 4.7

Anthropic
Kraken Reviews

Kraken

Big Squid
Claude Fable 5 Reviews

Claude Fable 5

Anthropic
Claude Opus 4.8 Reviews

Claude Opus 4.8

Anthropic