---
license: apache-2.0
base_model: Qwen/Qwen3-VL-4B-Instruct
tags:
- reward_model
- rbm
- robometer
- mimicgen
library_name: transformers
---

# Robometer-4B Full Fine-tune — MimicGen threading_d0

Full-parameter fine-tune of [`robometer/Robometer-4B`](https://huggingface.co/robometer/Robometer-4B)
(Qwen3-VL-4B-Instruct) on MimicGen `threading_d0` rollouts. Emits, per frame, a
**progress** value in [0,1] and a **success probability**.

This is the no-PEFT counterpart of
[`chomeed/robometer-4b-lora-threading-d0`](https://huggingface.co/chomeed/robometer-4b-lora-threading-d0):
identical data, schedule, and eval config — the only change is `model.use_peft=false`.

- **4.03B trainable params (90.7%)** — language model + all three prediction heads; the vision
  tower stays frozen (`train_vision_encoder: false`), same as the LoRA run
- 1000 steps, batch 8, lr 2e-5, cosine, warmup 0.1, weight decay 0.01, grad-norm clip 10
- bf16 full fine-tuning via Unsloth; **34 GB peak** on one B200 (the LoRA run peaked at 14.7 GB)
- Uploaded checkpoint is **step 500**, the best of the run by mean(pearson, kendall_last)
- Data: [`chomeed/mimicgen_threading_d0_train_rfm`](https://huggingface.co/datasets/chomeed/mimicgen_threading_d0_train_rfm)
  (160 traj: 80 successful + 80 failure), evaluated on
  [`..._test_rfm`](https://huggingface.co/datasets/chomeed/mimicgen_threading_d0_test_rfm) (40 traj, disjoint seeds)

## Results (held-out, 40 trajectories with unseen seeds)

Best checkpoint of each run, scored by the trainer's own eval:

| metric | full FT (step 500) | LoRA (step 600) |
|---|---|---|
| reward-alignment Pearson | **0.9804** | 0.9753 |
| policy-ranking Kendall (last) | **1.000** | **1.000** |
| ranking acc, failure vs successful | **1.000** | **1.000** |
| reward-alignment eval loss | **3.236** | 3.390 |
| success AUPRC | 0.162 | 0.192 |

Matched-step comparison at step 1000, so the two runs are compared at equal training budget:

| metric @ step 1000 | full FT | LoRA |
|---|---|---|
| Pearson | **0.9758** | 0.9749 |
| success-head AUROC (threshold-free) | **0.9944** | 0.9907 |
| eval loss | **3.208** | 3.329 |

Full fine-tuning wins consistently but by a small margin — Pearson is higher at every matched
eval step, and eval loss is lower throughout. Kendall is saturated at 1.000 for both, so this
test set does not separate them on trajectory ranking.

**Success-head calibration:** both models emit very low absolute success probabilities
(peak ≈0.002 on successful held-out trajectories). Ranking is excellent (AUROC 0.99) but the
probabilities are not calibrated — threshold well below 0.01, or just use the progress signal.

## Usage

> **Important:** this is a full-weights checkpoint, like the upstream `robometer/Robometer-4B`.
> It keeps fp32 `pixel_values` against bf16 vision blocks, which is fine under the Trainer's
> autocast but raises `RuntimeError: expected scalar type BFloat16 but found Float` at plain
> inference. **Wrap the forward pass in `torch.autocast`.** (LoRA checkpoints work either way.)

```python
import numpy as np, torch
from robometer.data.dataset_types import ProgressSample, Trajectory
from robometer.evals.eval_server import compute_batch_outputs
from robometer.utils.save import load_model_from_hf
from robometer.utils.setup_utils import setup_batch_collator

device = torch.device("cuda")
exp_config, tokenizer, processor, model = load_model_from_hf(model_path="chomeed/robometer-4b-full-threading-d0", device=device)
model.eval()
collator = setup_batch_collator(processor, tokenizer, exp_config, is_eval=True)

frames = ...  # uint8 (T, H, W, C), RGB, third-person view
traj = Trajectory(frames=frames, frames_shape=tuple(frames.shape), task="Pick up the needle and thread it through the hole in the tripod.",
                  id="0", metadata={"subsequence_length": int(frames.shape[0])}, video_embeddings=None)
batch = collator([ProgressSample(trajectory=traj, sample_type="progress")])
inputs = {k: (v.to(device) if hasattr(v, "to") else v) for k, v in batch["progress_inputs"].items()}

with torch.no_grad(), torch.autocast("cuda", dtype=torch.bfloat16):   # <-- required
    out = compute_batch_outputs(model, tokenizer, inputs, sample_type="progress",
                                is_discrete_mode=True, num_bins=10)   # progress head is discrete, 10 bins

progress = np.array(out["progress_pred"][0])                     # (T,) in [0,1]
success  = np.array(out["outputs_success"]["success_probs"][0])  # (T,) in [0,1]
```

Serving: `python robometer/evals/eval_server.py model_path=chomeed/robometer-4b-full-threading-d0`

## Reproducing

```bash
TASK=mimicgen_threading_d0 GPU=0 STEPS=1000 bash run_full_task.sh
```

Requires `robometer/data/dataset_success_cutoff.txt` to contain
`mimicgen_threading_d0_train_rfm,1.0` and `mimicgen_threading_d0_test_rfm,1.0` (simulation
trajectories have exact endpoints), and the RFM datasets preprocessed via
`robometer/configs/preprocess_threading_d0.yaml`.