Sential Innovations A division of Sential

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The Hikaru Series.

The testbed for our central idea. Hikaru exists to find out whether a model trained with Explanatory Reinforcement Learning behaves measurably differently from one trained on a binary reward.

What the series is for

Before we build anything large, we want to know that our training method works. Hikaru is that proof. Each model in the series is a finetune of an existing base, onto which we apply our ERL training stage. That keeps the experiment focused on what ERL changes, rather than entangling the results with the cost and variance of pretraining.

Hikaru is deliberately small and deliberately honest about its scope. It is not the model we hope to ship to the world; it is the model that tells us whether the rest of the plan is sound.

Models

One model, in development.

Hikaru-1

First model in the series

In development

A finetune of IBM Granite 4.1 (8B), trained with Explanatory Reinforcement Learning. The first concrete test of whether ERL produces a measurably different model.

Hikaru-1 exists to answer a single question: does a model trained with explanatory reinforcement learning behave measurably differently than one trained with a binary reward? Until that question has a measured answer, larger ambitions would be built on assumption. Hikaru-1 removes the assumption.

  • BaseIBM Granite 4.1 (8B), finetuned
  • MethodExplanatory Reinforcement Learning (ERL)
  • StatusIn development since June 16, 2026
  • PurposeValidate ERL before scaling to larger models

Read the development announcement →

Sential Innovations is an independent community and is not affiliated with, sponsored by, or endorsed by the makers of the base model. Any release will follow the base model's original licence, with attribution, and will be provided free and as-is.

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