Results
OMR 6-image inline validation (mmmu val, mathvista testmini, mathverse testmini Text-Dominant, wemath testmini, charxiv reasoning-qa, dynamath test). overall-6 = unweighted mean. Metric = accuracy.
Keeper = global_step_25 (peak):
Table with columns: metric, overall-6, mmmu, mathvista, mathverse, wemath, charxiv, dynamath| metric | overall-6 | mmmu | mathvista | mathverse | wemath | charxiv | dynamath |
|---|
| baseline peak @25 | 0.6681 | 0.6311 | 0.8019 | 0.8404 | 0.7437 | 0.3930 | 0.5986 |
| stock Qwen3-VL-8B ckpt-0 | 0.659 | 0.629 | 0.811 | 0.824 | 0.723 | 0.396 | 0.570 |
| baseline @125 (collapse) | 0.4994 | 0.5021 | 0.6796 | 0.6995 | 0.6184 | 0.2010 | 0.2958 |
Full trajectory (overall-6): 0.668 @25 → 0.644 @50 → 0.620 @75 → 0.620 @100 → 0.499 @125 (monotone decline; the step-125 drop is catastrophic — charxiv halved, dynamath collapsed). Run died to a vLLM deepstack crash after step 125, but the model was already degrading.
Training
- Base model:
Qwen/Qwen3-VL-8B-Instruct (cold-start, resume_mode=auto).
- Framework: fork of volcengine/verl —
ngquangtrung57/verl@videorl-mods. Fully-async GRPO: FSDP2 trainer + vLLM rollouter.
- Warm start: none (cold-start from stock 8B-Instruct).
- Reward: dapo-style
score = 0.8·accuracy + 0.2·format (FORMAT_WEIGHT=0.2, FORMAT_MIN_THINK_CHARS=100). No KL penalty.
- Exploration: OFF (this is the bitwise baseline against
omr-8b-grpo-ppexplore).
- 4-node 2+2 — 2 trainer nodes (16-GPU FSDP2, dp=16) + 2 rollout nodes (16 GPU, vLLM TP=2 → 8 replicas). H100×8 per node.
W&B
Project verl_fully_async (entity quangtrung5705-nanyang-technological-university-singapore):
https://wandb.ai/quangtrung5705-nanyang-technological-university-singapore/verl_fully_async/runs/gkiiopep
Intended use / limitations
Research checkpoint — the cold-start GRPO baseline in a controlled exploration study. Useful mainly as the A/B reference for omr-8b-grpo-ppexplore (the winner). It only marginally beats the zero-shot base (0.668 vs 0.659) at its peak and is unstable (collapses to 0.499 if trained past ~step 100); for actual use prefer the ppexplore checkpoint. Math / visual-reasoning image QA, <think>…</think> then-answer format. No safety/RLHF alignment beyond the base.
Usage
from transformers import AutoModelForImageTextToText, AutoProcessor
from PIL import Image
model_id = "ngqtrung/omr-8b-grpo-base"
model = AutoModelForImageTextToText.from_pretrained(model_id, dtype="auto", device_map="auto")
processor = AutoProcessor.from_pretrained(model_id)
messages = [{
"role": "user",
"content": [
{"type": "image", "image": Image.open("problem.png")},
{"type": "text", "text": "Solve the problem. Think step by step inside <think>...</think>, then give the final answer."},
],
}]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors="pt"
).to(model.device)
out = model.generate(**inputs, max_new_tokens=2048)
print(processor.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0])
Citation / lineage
- Base model: Qwen3-VL-8B-Instruct (Qwen team). Inherits the Qwen3-VL license — review the base model's terms; the Apache-2.0 tag refers to this repo's RLVR training artifacts.
- Framework: verl (volcengine/verl), fork
ngquangtrung57/verl@videorl-mods; fully-async GRPO (FSDP2 + vLLM).
- Study: controlled OMR/Video exploration study on Qwen3-VL-8B; this is the no-exploration OMR baseline arm (
docs/experiments_summary_8b.md).