Research · ORCNEIT
Introducing Research on ORCNEIT Lab
Our first two papers examine actual training of our language model: a base-training run and dialogue fine-tuning. Each includes measurements, charts, methods and limitations.
Published:
From plans to measurements
The ORCNEITGPT page describes the system we want to build. The new section answers a different question: what have we tested, and what did we observe? Neither account can substitute for the other.
Research presents each study separately: its question, test conditions, measurements, findings and limitations. Experiment dates and website publication dates are shown separately. Chart values are available in tables and as CSV downloads.
First paper: base-model training
We report a run conducted on 22–23 August 2026. The model started from a fresh random initialization and completed 80,000 steps. It processed 327.68 million training tokens, including repeated exposure, not that amount of unique text.
The paper shows training and development loss alongside a separate capability evaluation. Training completed, but the model failed the combined quality gate. That finding belongs beside the learning curve: lower prediction loss does not establish a ready assistant.
Second paper: the effects of dialogue fine-tuning
Our second study compares the base and fine-tuned models on identical tasks. Fine-tuning improved termination and reduced severe degeneration, but too few tasks were solved correctly for assistant acceptance.
This is not a finished-product announcement. It is a specific experiment involving weight updates, measured improvements and unresolved problems.
Two papers with methods, charts and result tables.
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