Yeongtak/Qwen2.5-Omni-7B-Omni-Persona-RLVR

Qwen2.5-Omni-7B — Omni-Persona RLVR

This repository hosts the 7B checkpoint. The card below documents both released checkpoints (3B and 7B) so the recipe and results can be compared side by side. (Qwen2.5-Omni-3B / 7B)

Two omnimodal checkpoints post-trained with RLVR for grounded personalization on the Omni-Persona benchmark. Both are fully merged weights — no adapter, load them like the base model.

Release	Base	Params	Size on disk	Files
Qwen2.5-Omni-3B-Omni-Persona-RLVR	Qwen/Qwen2.5-Omni-3B	5.5 B	12 GB	3 safetensors shards
Qwen2.5-Omni-7B-Omni-Persona-RLVR	Qwen/Qwen2.5-Omni-7B	10.7 B	21 GB	5 safetensors shards

Both ship the full omni preprocessor stack (preprocessor_config.json, video_preprocessor_config.json, chat_template.jinja), so they drop into the same vLLM and transformers paths as the corresponding base model.

Intended use

Research on omnimodal personalization: routing a query to the right persona across image, audio and text memory contexts, then answering from that persona's context or abstaining when the answer is not supported. Evaluated zero-shot on the Omni-Persona 750-item test set.

Out of scope. These are research artifacts, not products. Do not deploy them for person identification, biometric verification, speaker authentication, or any decision about a real individual. They were trained on synthetic personas and are not validated on real-world identity distributions. See Limitations.

Training recipe

Setting	Value
Algorithm	GSPO (RLVR, verifiable-reward policy optimization)
Reward mixture (v20)	30 : 30 : 40 = localization : verification : text-QA
Outcome rewards	asymmetric — r(TP) = r(TN) = +1.0, r(FN) = r(FP) = −0.5
Checkpoint	step 100
Bias term	none (nobias variant)
Base	Qwen/Qwen2.5-Omni-{3B,7B}
Training data	synthetic personas (TTS voices + generated faces); the benchmark is held out

The asymmetric reward is the design choice that drives the behaviour below. Correct answers and correct abstentions are rewarded equally (+1.0), while both error types are penalized at half magnitude (−0.5). Because a wrong attempt and a wrong abstention cost the same, and attempting is the higher-expected-value action whenever the model has any signal, the policy drifts toward answering.

Training code is not part of this release ("coming soon"). This card documents the recipe so the released checkpoints are interpretable; it is not sufficient to re-run the training.

Evaluation results

Omni-Persona v2.2, all 750 items, guarded protocol (an answerable item is correct only if the model does not abstain and the judge returns CORRECT). All values ×100.

Model	Ans ↑	Cal ↑	1-FA ↑	TA ↑
Qwen2.5-Omni-3B (base)	34.0	36.7	74.9	39.6
Qwen2.5-Omni-3B + RLVR (v20)	40.9	34.1	84.7	26.7
Qwen2.5-Omni-7B (base)	38.6	30.9	82.1	22.6
Qwen2.5-Omni-7B + RLVR (v20)	47.8	28.4	98.0	7.2

• Ans — guarded accuracy on the 391 answerable items
• Cal — count-weighted calibrated accuracy over all 750 items
• 1-FA — attempt rate on answerable items (1 − false-abstention rate)
• TA — true-abstention rate on the 359 unanswerable items

Judge non-determinism moves Ans/Cal by up to ~1 point; 1-FA and TA are keyword-based and reproduce near-exactly.

Reproduce with the released evaluation code:

bash scripts/serve_model.sh <path-or-hub-id-of-this-checkpoint>
bash scripts/run_eval.sh \
    --data omni-persona-data/omni_persona_v2_2.jsonl \
    --asset-root omni-persona-data/assets/lsd \
    --model <served-model-name> \
    --judge-base-url http://localhost:8091/v1

Limitations

RLVR raises answerable recall but does not improve calibrated accuracy. This is the headline caveat and it is visible in every row above:

• 3B: Ans +6.9 and 1-FA +9.8, but TA −12.9 and Cal −2.6.
• 7B: Ans +9.2 and 1-FA +15.9, but TA −15.4 and Cal −2.5.

The 7B checkpoint attempts 98.0% of answerable items and abstains correctly on only 7.2% of unanswerable ones — it has very nearly stopped abstaining at all. The gain on Ans is therefore partly a shift along the precision/abstention trade-off rather than better grounding. If your application needs a model that knows when to decline, the base checkpoints are the better starting point. These checkpoints are released to make that trade-off reproducible and studyable, not because they dominate the baseline.

Other limitations:

• English only. Not evaluated in any other language.
• Synthetic-to-real gap. Training used synthetic faces and TTS voices; the benchmark uses real images and partly real speech. Behaviour on other real-world identity distributions is unmeasured.
• Fixed context shape. Tuned against 4 interleaved image+audio+text contexts in a single user turn with temperature=0.0, max_tokens=256. Longer or differently-shaped contexts are out of distribution.
• Inherited base-model limitations. All failure modes of Qwen2.5-Omni — hallucination, fine- grained speaker/face confusion, safety behaviour — carry over and were not separately mitigated.
• No safety post-training. The RLVR objective rewards benchmark correctness only. Assume the base model's safety alignment is degraded, not preserved.
• Single seed, single evaluation set. No variance estimates across seeds are reported here.

License and attribution

⚠️ Decision required before publishing. These are derivative works of Qwen2.5-Omni. Both Qwen/Qwen2.5-Omni-3B and Qwen/Qwen2.5-Omni-7B are published on the Hub under license: other — a Qwen-specific license, not a blanket Apache-2.0 grant. Before release:

1. Read the LICENSE/NOTICE file in each base-model repo and confirm the exact license name, whether it is research-only or permits commercial use, and whether the 3B and 7B terms differ (they are distinct repos and may carry distinct terms).
2. Set license_name / license_link in the frontmatter of each model repo to the verified base-model license — do not leave the placeholder above.
3. Copy the base model's LICENSE file into each checkpoint repo, and keep any required attribution/naming notice.
4. State the derivative relationship in the repo body (already done via base_model:).

Base model citation:

@article{xu2025qwen2,
  title   = {Qwen2.5-Omni Technical Report},
  author  = {Qwen Team},
  journal = {arXiv preprint arXiv:2503.20215},
  year    = {2025}
}

Citation

@article{oh2026omni,
  title   = {Omni-Persona: Systematic Benchmarking and Improving Omnimodal Personalization},
  author  = {Oh, Yeongtak and Lee, Dongwook and Park, Sangkwon and Kim, Heeseung and Yoon, Sungroh},
  journal = {arXiv preprint arXiv:2605.09996},
  year    = {2026}
}