Results
Offline full-set eval (VideoMME-v1 2700 + PerceptionComp 1108 + Video-Holmes 1837 = 5645 rows), scored with the repo's vero compute_score. mean = macro-mean of the 3 bench accuracies.
Keeper = global_step_180 (peak):
Table with columns: metric, mean, videomme, holmes, perceptioncomp, format| metric | mean | videomme | holmes | perceptioncomp | format |
|---|
| SFT-770 RL @180 (keeper) | 0.4845 | 0.6574 | 0.4513 | 0.3448 | 0.983 |
| cold-base RL keeper @80 (sibling) | 0.4918 | 0.6581 | 0.4741 | 0.3430 | — |
| stock Qwen3-VL-8B ckpt-0 (zero-shot) | 0.4444 | 0.6426 | 0.4143 | 0.2762 | — |
Trajectory (mean): flat plateau ~0.481 (±0.01) over steps 60–160, peak 0.4845 @180, then edged down (0.467 @200, 0.471 @220). Format compliance saturated ~0.97–0.98 throughout. The high reward did not convert to held-out accuracy — visible only because of the offline full-set eval.
Training
- Base model:
Qwen/Qwen3-VL-8B-Instruct, warm-started from the OMR SFT checkpoint qwen3vl8b_ommr_sft_3node/checkpoint-770-hf. (SFT-770 = the stock 8B SFT'd on OpenMMReasoner reasoning data with lmms-engine — a math/visual-reasoning SFT, not a video model — used here to seed video RL.)
- Framework: fork of volcengine/verl —
ngquangtrung57/verl@videorl-mods. Fully-async GRPO: FSDP2 trainer + vLLM rollouter.
- Reward: dapo-style
score = 0.8·accuracy + 0.2·format (FORMAT_WEIGHT=0.2, FORMAT_MIN_THINK_CHARS=100). No KL penalty.
- Exploration: OFF.
- Topology: 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). Train metrics only — video val is offline, not on W&B:
https://wandb.ai/quangtrung5705-nanyang-technological-university-singapore/verl_fully_async/runs/s59ethn1
Intended use / limitations
Research checkpoint — the SFT-warmstart arm of an 8B video study whose headline finding is a 7-way dead-heat at ~0.485 full-set val. This run shows SFT warm-start gives faster early convergence but no durable val edge over cold-start (which actually peaks slightly higher at 0.4918). Multiple-choice video QA, <think>…</think> then-answer format. No safety/RLHF alignment beyond the base.
Usage
from transformers import AutoModelForImageTextToText, AutoProcessor
model_id = "ngqtrung/video-8b-grpo-sft770"
model = AutoModelForImageTextToText.from_pretrained(model_id, dtype="auto", device_map="auto")
processor = AutoProcessor.from_pretrained(model_id)
messages = [{
"role": "user",
"content": [
{"type": "video", "video": "clip.mp4"},
{"type": "text", "text": "Answer the multiple-choice question. Reason inside <think>...</think>, then give the final letter."},
],
}]
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=1024)
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.
- Warm start: OMR-SFT-770 (OpenMMReasoner SFT of Qwen3-VL-8B-Instruct, trained with lmms-engine).
- Framework: verl (volcengine/verl), fork
ngquangtrung57/verl@videorl-mods; fully-async GRPO (FSDP2 + vLLM).
- Study: controlled OMR/Video exploration study on Qwen3-VL-8B; SFT-warmstart video arm (
docs/experiments_summary_8b.md).