Hikaru-1 enters development.

Sential Innovations has begun development of Hikaru-1, its first model. Hikaru-1 is a finetune of IBM Granite 4.1 (8B), trained using our research method, Explanatory Reinforcement Learning (ERL).

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

Why a finetune

Hikaru-1 is not a model trained from scratch. It begins from IBM Granite 4.1 (8B) and applies the ERL training stage on top. This keeps the experiment focused on what ERL changes, rather than entangling results with the cost and variance of pretraining. A manageable base lets us run, measure, and iterate quickly.

What ERL changes

Conventional reinforcement learning gives the model a binary signal: a response was good, or it was not. ERL replaces that with three things:

• A graded scale from −5 to +5, with 0 for neutral, so the model learns magnitude and direction, not only verdict.
• Per-dimension scores, so a response can be helpful but cold, or accurate but evasive, and be corrected on each independently.
• A grader explanation written for every score, the reasoning behind the correction, which the model learns alongside the score itself.

The hypothesis is that a model trained this way learns transferable principles rather than narrow patterns. Hikaru-1 is the first chance to see whether that holds.

What happens next

Development is underway. The near-term plan is to complete the ERL training run on Hikaru-1, then evaluate it against a baseline trained with conventional RLHF. Results, when we have them and are confident in them, will be published on our research page.

We will share more when there is something concrete to report. Not before.