FFT finetune of Robometer-4B on both so101 datasets (armnet + molmoact)

89f8382 verified 17 days ago

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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 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.