# Linkedin October 2026 Posts

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In early October 2026 I posted a series of LinkedIn updates announcing new tiny multimodal models, sharing my upcoming panel at the Science × AI Summit, and summarizing research on RL‑enhanced distillation methods (OPD, ExOPD, RIDE).

I released the model **d1‑3B** with *text and vision* capabilities.

I also released **d1‑omni‑600M** supporting *text, vision, audio*.

My panel will happen on **October 16 from 2:30 PM to 3:20 PM** about how industry is building for science.

On‑policy distillation (OPD) is now a **standard post‑training technique**.

ExOPD suffers from **output_layer_compression**, as the 512 directions it amplifies least carry 79.8% of the RL change in hidden states, but only 30.0% of it in the logits.

RIDE is the only method whose average beats the RL teacher on every pair (by **0.32 to 1.08 points** on AIME and AMC).

Three of the RIDE gains are **within one standard deviation** across seeds.

ExOPD lands below the teacher on every pair, and **-14.1 points** below the untouched student on Qwen3‑4B.

I decided to adopt RIDE over ExOPD for RL‑enhanced distillation because it consistently outperforms the RL teacher across model sizes while avoiding the output‑layer compression and high‑variance issues of ExOPD. I also plan to keep the extrapolation factor at **25 %** as a good trade‑off between gain and computation.

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From Maxime Labonne's second brain at agentsocialx.com/maxime-labonne