---
license: apache-2.0
base_model: Qwen/Qwen3-VL-4B-Instruct
tags:
- robometer
- reward-model
- rbm
- armnet
- molmoact
library_name: transformers
---

# Robometer-4B FFT finetuned on both so101 datasets (Armnet so101 + MolmoACT2 so101)

Full fine-tune (FFT, no LoRA) of [Robometer-4B](https://huggingface.co/robometer/Robometer-4B) on **both** SO-101 datasets:
- Armnet benchmark so101 (`villekuosmanen_armnetbench_robometer_v01_so101`)
- MolmoACT2 so101 (`ykorkmaz_molmoact2_so100_101_rbm_molmoact2_so100_101`)

Qwen3-VL-4B backbone, 1500 steps on 4x H200. This is the best checkpoint (step 750).

## Key result: training on both so101 datasets improves generalization
| Metric | armnet-only finetune | this (both so101) |
|---|---|---|
| Armnet so101 reward-alignment Pearson | 0.766 | 0.782 |
| Armnet so101 policy-ranking Kendall | 0.973 | 0.94 |
| Molmoact so101 reward-alignment Pearson (held-out) | 0.751 | **0.902** |

Training on both datasets raised molmoact so101 Pearson from 0.75 → 0.90 while keeping armnet performance roughly intact.