Qwen3-VL-8B · OMR · GRPO + ppexplore (τ0.95) — campaign winner

RLVR post-training of Qwen/Qwen3-VL-8B-Instruct on the OMR (OpenMMReasoner, math/visual-reasoning) data with fully-async GRPO and an entropy-aware token-dropout exploration ("ppexplore") at the rollout stage. This checkpoint is experiment_name=grpo_omr_4node_full_v1_8b_base_ppexplore_v1, global_step_250 — the best 8B OMR checkpoint of the whole controlled-exploration study.

This is the winner of the campaign: exploration (entropy-aware token-dropout, τ=0.95) reaches OMR overall-6 val accuracy 0.714 @ step 250, +4.6 pt over the cold-start GRPO baseline's peak of 0.668, and — critically — it stabilizes training: the baseline collapsed to 0.499 by step 125, while this run never dropped below 0.659 over 225 steps. Exploration was warm-started from the baseline's global_step_50.

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 of the 6 benches. Metric = accuracy (weight-independent).

Keeper = global_step_250:

Exploration wins every benchmark, largest on wemath (+6.9) and mathverse (+5.7). Val trajectory: 0.681 @75 → 0.673 @100 → 0.659 @150 → 0.668 @200 → 0.714 @250 → 0.661 @300 (never below 0.659 over 225 steps; baseline by contrast fell 0.668→0.620→0.499).

Training

• Base model: Qwen/Qwen3-VL-8B-Instruct.
• Framework: fork of volcengine/verl — ngquangtrung57/verl@videorl-mods. Fully-async GRPO: FSDP2 trainer + vLLM rollouter, partial rollout, staleness-bounded off-policy.
• Warm start: from the cold-start OMR baseline (grpo_omr_4node_full_v1_8b_base_perf) global_step_50 (resume_mode=resume_path).
• Reward: dapo-style score = 0.8·accuracy + 0.2·format (FORMAT_WEIGHT=0.2, FORMAT_MIN_THINK_CHARS=100). No KL penalty (use_kl_in_reward=false, use_kl_loss=false).
• 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.
• Batch: ppo_mini_batch_size=16 × require_batches=4 × rollout.n=8 = 512 trajectories/step.
• Optim / seq: lr 1e-6, warmup 25 steps; total_epochs=2; clip_ratio 0.2 / 0.3 (clip_c=10.0); max_prompt_length=2048, max_response_length=16384; enforce_eager=true; gpu_memory_utilization=0.75; staleness 0.5.
• Exploration block (entropy-aware token-dropout at rollout; byte-identical to the OMR-4B τ0.95 block that won +1.4 pt):

• Train metrics: ~216 s/step; final reward 0.658 (lower than baseline by design — exploration tokens score below the greedy anchor); final response_length ~1947 tok. Run died at step 335 to a repeatable verl resume_path hang (4× confirmed) — keeper step_250 is well before that.

W&B

Project verl_fully_async (entity quangtrung5705-nanyang-technological-university-singapore). The logical run spans two crash-resume segments:

• steps 75–124: https://wandb.ai/quangtrung5705-nanyang-technological-university-singapore/verl_fully_async/runs/i56xzqqo
• steps 125–335 (main, contains the step-250 keeper): https://wandb.ai/quangtrung5705-nanyang-technological-university-singapore/verl_fully_async/runs/hi1ng1nx

Intended use / limitations

Research checkpoint from a controlled exploration study (does token-dropout exploration help multimodal RLVR?). On OMR-8B the answer is a clear yes: this is the campaign winner (+4.6 pt and training-stability rescue). Best for math / visual-reasoning image QA in a <think>…</think> then-answer format. Not a general-purpose chat model; not tuned for video (see the video-8b-grpo-* siblings, which are a documented dead-heat at ~0.485). No safety/RLHF alignment beyond the base model.

Usage

from transformers import AutoModelForImageTextToText, AutoProcessor
from PIL import Image

model_id = "ngqtrung/omr-8b-grpo-ppexplore"
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). This checkpoint inherits the Qwen3-VL license — review the base model's terms before use; 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).
• Method: entropy-aware token-dropout exploration ("ppexplore", τ=0.95) at the rollout stage, warm-started from a mid-RL checkpoint. Part of a controlled OMR/Video exploration study on Qwen3-VL-8B (docs/experiments_summary_8b.md).