An Open Recipe for Frontier Multimodal Search Agents

Cold-Start Agentic SFT  ·  Multi-Turn Fatal-Aware GRPO  ·  Visual Tool Use

📑 Table of Contents

• 📖 Introduction
• 🗺️ Overview
• 🍭 Method Overview
• 📊 Main Results
• 🔎 Case Study
• 📁 Repository Layout
• 🛠️ Prerequisites
• 🏋️ Agentic SFT · code/SFT
• 🚀 Agentic RL · code/RL
• 📊 Inference & Evaluation · opensearch_vl
• 🚧 TODO
• 🙌 Acknowledgements

---

📖 Introduction

OpenSearch-VL is a fully open recipe for training frontier multimodal deep-research agents with agentic reinforcement learning. In contrast to standard VLMs that answer in a single forward pass, the agent operates as a closed loop: it inspects the image, crops or enhances the regions of interest, issues web and image searches, visits the retrieved pages, and only then writes an answer grounded in the gathered evidence.

Reproducing top-tier multimodal search agents has so far been difficult because the underlying training data, trajectory-synthesis pipelines, and training recipes remain proprietary. This release aims to close that gap: we open-source the data, code, and model checkpoints required to reproduce the paper end-to-end.

The recipe addresses three challenges that we found to be largely independent in practice:

• Data. A curation pipeline built on top of the Wikipedia hyperlink graph synthesizes image-grounded multi-hop VQA. Fuzzy entity rewriting and source-anchored visual grounding are used to suppress shortcut solutions in which a single retrieval step is sufficient. The pipeline yields two open datasets: SearchVL-SFT-36k for supervised fine-tuning and SearchVL-RL-8k for reinforcement learning.
• Tools. A unified visual and retrieval tool environment (crop, layout_parsing, text_search, image_search, web_search, visit, perspective_correct, super_resolution, sharpen, python_interpreter) is shared across SFT data generation, RL rollout, and inference. This allows the agent both to recover from imperfect visual inputs and to acquire external knowledge through a consistent interface.
• Algorithm. A multi-turn fatal-aware GRPO algorithm explicitly handles cascading tool failures during long rollouts. Tokens that follow a fatal step are masked out of the policy gradient, while one-sided advantage clamping preserves the credit assigned to valid pre-failure reasoning rather than penalizing the entire trajectory.

Across seven knowledge-intensive multimodal benchmarks—SimpleVQA, VDR, MMSearch, LiveVQA, BrowseComp-VL, FVQA, and InfoSeek—OpenSearch-VL improves the average score by more than 10 points over the corresponding agentic baselines, and at the 30B / 32B scale matches the accuracy of strong proprietary systems.

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🗺️ Overview

This repository provides everything needed to reproduce, fine-tune, and evaluate OpenSearch-VL:

Component	Path	Description
SFT Training	SFT/	Agentic cold-start with LLaMA-Factory + Ray + ZeRO-3 (full-parameter fine-tune of LLM + ViT + projector)
RL Training	RL/	Asynchronous agentic RLOO/GRPO on top of SFT, built on rLLM + verl + Megatron-LM + sglang
Inference & Evaluation	opensearch_vl/	Unified run_infer.py --model {8b,30b-a3b,32b,claude} rollout + GPT-4o judge for BrowseComp-VL, HLE, VDR-Bench
Models	OpenSearch-VL	OpenSearch-VL-{8B, 30B-A3B, 32B} checkpoints
Datasets	OpenSearch-VL	SearchVL-SFT-36k (cold-start) and SearchVL-RL-8k (RL)

Workflow at a Glance

   ┌────────────────┐     ┌────────────────┐     ┌────────────────────┐
   │ Qwen3-VL base  │ ─── │ Agentic SFT    │ ─── │ Async Agentic RL   │ ───▶ OpenSearch-VL
   │ (HF weights)   │     │ (code/SFT)     │     │ (code/RL)          │
   └────────────────┘     └────────────────┘     └────────────────────┘
                                │                         │
                                ▼                         ▼
                        SearchVL-SFT-36k          SearchVL-RL-8k
                        7-domain tool-use         Vision-DeepResearch-QA
                        cold-start trajectories   (RLOO / GRPO + fatal-aware)

Tool Environment

OpenSearch-VL is equipped with a heterogeneous tool set $\mathcal{T} = \mathcal{T}_v \cup \mathcal{T}_r$ shared by SFT, RL, and inference:

Category	Tools	Purpose
Retrieval ($\mathcal{T}_r$)	text_search, image_search, web_search, visit	Acquire external textual / visual evidence and visit pages
Image Enhancement ($\mathcal{T}_v$)	sharpen, super_resolution, perspective_correct	Repair blurry, low-resolution, or skewed inputs before retrieval
Attention & Parsing ($\mathcal{T}_v$)	crop, layout_parsing (OCR)	Localize regions of interest and decode fine-grained content
Computation	python_interpreter	Numerical / programmatic computation on retrieved evidence

Quick Links

• Get started → Prerequisites
• Train your own SFT model → Agentic SFT
• Run agentic RL → Agentic RL
• Inference & benchmark → Inference & Evaluation

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🍭 Method Overview

Data Curation Pipeline. Starting from the English Wikipedia hyperlink graph, we sample multi-hop entity paths and convert them into multi-hop VQA instances by (a) extracting canonical question–answer pairs along the path, (b) rewriting each intermediate entity into a fuzzy descriptor while certifying answer invariance and uniqueness, and (c) anchoring the question on a representative image of the source node — not the answer node — so that single-hop image lookup shortcuts are eliminated. The pipeline finishes with staged tool-demanding filtering and an enhancement subset (random degradations paired with the corresponding restoration tools) before synthesizing multi-turn expert trajectories with answer- and process-level rejection sampling.

RL Training Pipeline. Starting from the SFT-initialized checkpoint, we sample a group of multi-turn trajectories against the real environment $\mathcal{E}$. Each trajectory is scored by a composite reward combining final-task accuracy ($r_{\text{acc}}$), process-level search-query quality ($r_{\text{query}}$), and a multiplicative format gate ($r_{\text{fmt}}$). To preserve valid reasoning when a trajectory eventually triggers cascading tool failures, we apply fatal-aware token masking to truncate the sequence at the fatal step $f_i$ and one-sided advantage clamping ($\hat{A}_i = \max(\widetilde{r}_i, 0)$ for fatal trajectories) during policy optimization, preventing the suppression of viable early reasoning.

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📊 Main Results

OpenSearch-VL is built on three Qwen3-VL variants and evaluated on 7 multimodal knowledge-intensive QA / web-search benchmarks under the same Pass@1 + GPT-4o judge protocol as VDR-Bench.

Highlights. OpenSearch-VL-8B is the strongest open 8B agent (+3.9 Avg over SenseNova-MARS-8B). OpenSearch-VL-30B-A3B improves the Qwen3-VL agentic baseline by +13.8 Avg, with large gains on VDR (+13.3), MMSearch (+24.5), FVQA (+10.2), and InfoSeek (+16.2). OpenSearch-VL-32B surpasses Gemini-2.5-Pro and Claude-4-Sonnet direct-reasoning baselines on most benchmarks.

Fatal-aware GRPO ablation. Vanilla search-augmented GRPO improves SFT 64.6 → 67.6 Avg; the hard-masking baseline of Vision-DeepResearch saturates at 67.7; fatal masking alone reaches 69.1; our full method with one-sided advantage clamping reaches 71.8 — a +4.2 gain over vanilla GRPO and the best score on every benchmark.

Fatal-aware GRPO sustains a higher number of tool-use turns and a higher batch accuracy than vanilla GRPO and the Hard-Mask baseline — encouraging productive exploration rather than prematurely suppressing difficult rollouts.

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🔎 Case Study

The example below illustrates a representative OpenSearch-VL trajectory on a knowledge-intensive visual question: “In what year did this bridge open?” The answer cannot be read directly from the image or reliably produced by parametric knowledge alone. Instead, the agent progressively grounds the query through tool use.

Tool-use flow.

1. Visual inspection — The agent identifies the roadside sign as the most useful visual clue.
2. Crop — It zooms into the sign to obtain a cleaner local view.
3. Image search — The cropped region helps identify the structure as the Kessock Bridge.
4. Text search / verification — A targeted search verifies the official opening year as 1982.

This case highlights the core behavior encouraged by OpenSearch-VL: the agent does not guess from a single model pass, but chains visual perception, image retrieval, and textual evidence acquisition until the answer is grounded.

---

📁 Repository Layout

code/
├── SFT/                           # agentic supervised fine-tuning (LLaMA-Factory fork)
│   ├── examples/agentic_full/     # training YAMLs for Qwen2.5-VL / Qwen3-VL / Qwen3.5-VL
│   ├── examples/deepspeed/        # ZeRO-2 / ZeRO-3 configs
│   ├── data/dataset_info.json     # 7 agentic-SFT datasets (relative paths)
│   ├── src/llamafactory/          # LLaMA-Factory source (trainer, data loader, CLI)
│   └── README.md                  # SFT-specific instructions
│
├── RL/                            # reinforcement learning on the SFT checkpoint
│   ├── rllm/                      # rLLM + verl + vision_deepresearch_async_workflow/
│   ├── Megatron-LM/               # Megatron backend
│   ├── mbridge/                   # HF ↔ Megatron parallelism bridge
│   └── README.md                  # RL-specific instructions
│
├── opensearch_vl/                 # inference + benchmark evaluation (first-party package)
│   ├── run_infer.py               # unified entrypoint (--model 8b|32b|30b-a3b|claude)
│   ├── run_infer.sh               # env-driven launcher around run_infer.py
│   ├── eval_with_gpt4o.py         # GPT-4o judge for BrowseComp-VL / HLE / VDR-Bench
│   ├── run_eval.sh                # env-driven judge driver across all benchmarks
│   ├── .env.example               # template for inference / judge / search keys
│   └── opensearch_infer/          # modular package: config, prompts, auth, tools,
│                                  # search, runners (Claude + Qwen3-VL dense / MoE),
│                                  # message converters, multi-turn pipeline
│
└── README.md                      # (this file)

---

🛠️ Prerequisites

Component	Minimum
Python	3.10+
CUDA	12.1+ (12.4 recommended)
PyTorch	≥ 2.4 with CUDA support
GPU	≥ 1× H100 / H800 / A100-80GB for 8B (multi-node for 30B / 32B)
NCCL / RDMA	InfiniBand / RoCE recommended for multi-node; see RL/rllm/.env.example

The three components share most of their Python dependencies (PyTorch, transformers, transformer_engine, flash-attn, deepspeed, ray, qwen-vl-utils, sglang) — we recommend installing each sub-project into its own virtual environment.

External API keys

All keys are optional; components gracefully no-op if unset.

Variable	Used by	Purpose
API_GATEWAY_HOST / API_GATEWAY_USER / API_GATEWAY_KEY	RL	Optional HMAC-secured gateway that proxies Serper + Jina behind one credential (set on RL workers).
API_HOST / API_USER / API_KEY	opensearch_vl	Same gateway, named to match the inference package's env vars.
SERPER_API_KEY	RL, opensearch_vl	Serper.dev text & image search (used when no gateway is configured).
JINA_API_KEY	RL, opensearch_vl	Jina AI reader (page visit / content extraction).
QWEN_API_BASE / QWEN_MODEL_NAME	opensearch_vl	OpenAI-compatible chat-completions server used for search summarization (defaults to a local Qwen3-32B).
LAYOUT_PARSING_API_URL / LAYOUT_PARSING_TOKEN	RL, opensearch_vl	PP-StructureV3-compatible OCR / layout endpoint.
CLAUDE_API_HOST / CLAUDE_API_USER / CLAUDE_API_KEY	opensearch_vl	Optional HMAC-secured gateway for the Claude Opus 4.5 backend.
JUDGE_API_BASE_URL / JUDGE_APP_ID / JUDGE_APP_KEY / JUDGE_MODEL_MARKER	opensearch_vl	OpenAI-compatible GPT-4o judge used by eval_with_gpt4o.py.
QWEN3VL_8B_PATH / QWEN3VL_32B_PATH / QWEN3VL_30B_A3B_PATH	opensearch_vl	Local checkpoints for the three Qwen3-VL variants (overrideable via --checkpoint).
FVQA_IMAGE_DIR	opensearch_vl	Optional fallback directory of <case_id>.<ext> images used when a benchmark URL is unreachable.
WANDB_API_KEY	SFT, RL	W&B logging.

Two templates are provided: RL/rllm/.env.example for the RL workers, and opensearch_vl/.env.example for inference + judge. Copy whichever applies and source it before launching.

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🏋️ Agentic SFT · code/SFT

Cold-starts the base VLM on 7 tool-use datasets (FVQA, Palace, WebQA, LiveVQA, WikiArt, Wiki-zh, Wiki-en — together forming SearchVL-SFT-36k, with an average of 6.3 tool-invocation turns per trajectory). We perform a full-parameter fine-tune of the LLM + vision tower + projector with DeepSpeed ZeRO-3, distributed via Ray.

Install

cd code/SFT
pip install -e ".[torch,metrics,deepspeed,ray]"
pip install qwen-vl-utils pillow av decord torchvision flash-attn

Data layout

Download the SearchVL-SFT-36k bundle from the HuggingFace collection and place the 7 sub-sets under code/SFT/data/ so that the relative file_name values in data/dataset_info.json resolve:

SFT/data/
├── dataset_info.json
├── new_fvqa/fvqa_llama_factory_clean.json
├── palace/palace_llama_factory_filtered.json
├── WebQA/webqa_llama_factory_filtered.json
├── new_livevqa/livevqa_llama_factory_filtered.json
├── wikiart/wikiart_llama_factory_filtered.json
├── wiki_en/wiki_en_llama_factory_filtered.json
└── wiki_zh/wiki_zh_llama_factory_filtered.json

Each JSON is in ShareGPT format with conversations, images, system, and tools columns.

Launch

cd code/SFT

# Multi-node Ray (16 nodes × 8 GPU by default):
USE_RAY=1 llamafactory-cli train \
    examples/agentic_full/qwen3_vl_full_sft_8b_ray.yaml

# Single-node smoke test:
FORCE_TORCHRUN=1 NNODES=1 NPROC_PER_NODE=8 llamafactory-cli train \
    examples/agentic_full/qwen3_vl_full_sft_8b_ray.yaml

Available training configs

Edit ray_num_workers, placement_strategy, and NCCL / IB vars to match your cluster.

YAML	Model	# workers
qwen3_vl_full_sft_8b_ray.yaml	Qwen3-VL-8B-Instruct	256
qwen3_vl_full_sft_30_3b_ray.yaml	Qwen3-VL-30B-A3B-Instruct	256
qwen3_vl_full_sft_32b_ray.yaml	Qwen3-VL-32B-Instruct	256
qwen3_5vl_full_sft_27b_ray.yaml	Qwen3.5-VL-27B-Instruct	256
qwen3_5vl_full_sft_35b_3b_ray.yaml	Qwen3.5-VL-35B-A3B	256
qwen2_5_vl_full_sft_7b_ray.yaml	Qwen2.5-VL-7B-Instruct	256
qwen2_5_vl_full_sft_32b_ray.yaml	Qwen2.5-VL-32B-Instruct	256
qwen2_5_vl_full_sft_72b_ray.yaml	Qwen2.5-VL-72B-Instruct	256

Shared hyper-parameters

Hyperparameter	Value
Cutoff length	32000
Precision	bf16
Learning rate	2e-5 (cosine, warmup_ratio=0.1)
Epochs	8
Per-device batch	1 (with gradient_checkpointing: true)
DeepSpeed	ZeRO-3 (examples/deepspeed/ds_z3_config.json)
Frozen modules	none (freeze_vision_tower: false, freeze_multi_modal_projector: false)

Checkpoints land in saves/<model>/full/sft_data_v1/ (override via output_dir / ray_storage_path in the YAML).

Full details, dataset format, and cluster notes: code/SFT/README.md.

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🚀 Agentic RL · code/RL

Asynchronous agentic RLOO / GRPO / PPO on top of the SFT checkpoint, using rLLM's AgentWorkflowEngine, verl as the policy-optimization backend, and Megatron-LM + mbridge for large-scale model parallelism. Trajectories are rolled out by sglang; Megatron handles actor / ref updates.

Install

cd code/RL/rllm    && pip install -e .
cd ../Megatron-LM  && pip install -e .
cd ../mbridge      && pip install -e .
pip install "sglang[all]" transformer_engine flash-attn \
            ray==2.34.* hydra-core omegaconf wandb \
            pillow requests python-dotenv

Copy the env template:

cp RL/rllm/.env.example RL/rllm/.env   # edit keys as needed

Data preparation

The workflow expects rllm.data.DatasetRegistry to hold a dataset named Vision-DeepResearch-QA (i.e. SearchVL-RL-8k). Two helpers in RL/rllm/vision_deepresearch_async_workflow/data_prepare/ handle the conversion:

cd RL/rllm/vision_deepresearch_async_workflow/data_prepare

# 1) Extract embedded image bytes → PNG + JSONL
DATA_ROOT=./data/Vision-DeepResearch-RL-Data \
    bash convert_parquet2jsonl.sh

# 2) Register it with rLLM as "Vision-DeepResearch-QA" (90 / 10 split)
JSONL_PATH=./data/Vision-DeepResearch-RL-Data/vision-deepresearch_RL_Demo_1k.jsonl \
    bash register_rl_dataset.sh

Launch

All run scripts cd into rllm/, auto-source .env, and call python -m vision_deepresearch_async_workflow.train_deepresearch_workflow_megatron with the right Hydra overrides.

# Primary configuration in the paper: 8B dense, 8 nodes × 8 GPU
bash RL/rllm/vision_deepresearch_async_workflow/run/qwen3-vl-8b-multi-node.sh

# Other presets:
bash RL/rllm/vision_deepresearch_async_workflow/run/qwen3-vl-8b-single-node.sh    # 1-node smoke test
bash RL/rllm/vision_deepresearch_async_workflow/run/qwen3-vl-30b-3b-multi-node.sh # 30B-A3B MoE
bash RL/rllm/vision_deepresearch_async_workflow/run/qwen3-vl-32b-multi-node.sh    # 32B dense

Key hyper-parameters (8B multi-node)

Field	Value
Advantage estimator	rloo (set grpo / reinforce_plus_plus to swap)
KL coefficient	0.001
Clip ratio (high)	0.28
Train prompt batch	256 (group size n_resp_per_prompt=8, mini-batch 64)
Max prompt / response length	4096 / 70000
Megatron parallelism	TP=4 / PP=2 / CP=8 (dense)
sglang rollout	TP=4, gpu_memory_utilization=0.85
Reward composition	$r = r_{\text{fmt}} \cdot [,0.8, r_{\text{acc}} + 0.2, r_{\text{query}},]$
Fatal threshold $K$	3 consecutive tool-execution errors

Checkpoints go to checkpoints/${project_name}/${exp_name}/; trajectories can be dumped to $TRAJ_DUMP_DIR (default ./trajectory_dumps/<exp>/).

Reproducing the paper

Variant	Script	Cluster
OpenSearch-VL-8B	qwen3-vl-8b-multi-node.sh	8 × 8 H100 / H800
OpenSearch-VL-30B-A3B	qwen3-vl-30b-3b-multi-node.sh	8 × 8 H100 / H800
OpenSearch-VL-32B	qwen3-vl-32b-multi-node.sh	16 × 8 H100 / H800

Full details, environment variables, and cluster notes: code/RL/README.md.

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📊 Inference & Evaluation · opensearch_vl

Modular Python package that drives the trained model as a tool-using Visual Investigation Agent, plus a GPT-4o judge for standardized benchmark scoring. The same agent loop, tool environment and search/visual utilities are shared across all three Qwen3-VL backends and the optional Claude Opus 4.5 backend; the variant is selected with a single --model flag.

Package layout

opensearch_vl/
├── run_infer.py                  # unified entrypoint (--model 8b|32b|30b-a3b|claude)
├── run_infer.sh                  # env-driven wrapper around run_infer.py
├── run_eval.sh                   # env-driven judge driver across all benchmarks
├── eval_with_gpt4o.py            # GPT-4o judge for BrowseComp-VL / HLE / VDR-Bench
├── .env.example                  # full env-variable template
└── opensearch_infer/
    ├── config.py                 # env-driven settings + ModelSpec registry
    ├── prompts.py                # Visual Investigation Agent system prompt
    ├── auth.py                   # HMAC helper + Claude gateway client
    ├── cos_upload.py             # optional COS uploader bootstrap
    ├── image_io.py               # image download / decode / cache utilities
    ├── image_engines.py          # PIL + OpenCV crop / OCR / enhance pipelines
    ├── search.py                 # text_search / image_search / layout_parsing
    ├── tools.py                  # JSON tool schema + parsing + dispatcher
    ├── messages.py               # Gemini ↔ Claude / Qwen3-VL converters
    ├── runners.py                # ClaudeRunner + Qwen3VLRunner (dense + MoE)
    └── pipeline.py               # per-case multi-turn agent loop

Rollout

One entrypoint, four backends. Each call accepts a parquet of questions + images and writes one trajectory JSON per sample:

--model	Backend
8b	OpenSearch-VL-8B (Qwen3-VL-8B base, dense)
32b	OpenSearch-VL-32B (Qwen3-VL-32B base, dense)
30b-a3b	OpenSearch-VL-30B-A3B (Qwen3-VL-30B-A3B base, MoE)
claude	Claude Opus 4.5 via HMAC gateway (no GPU required)

Multi-GPU model parallelism is enabled automatically when --gpus 0,1,... lists more than one device (device_map="auto"); single-GPU placement uses device_map={"": "cuda:N"}. The MoE scatter dtype patch for 30B-A3B is applied automatically.

# Source the env template first; only the entries you need have to be filled in.
cp opensearch_vl/.env.example ~/.opensearch-vl.env
source ~/.opensearch-vl.env

# Dense Qwen3-VL-8B on a single GPU
python opensearch_vl/run_infer.py --model 8b --gpus 0 \
    --data-path  /path/to/benchmark.parquet \
    --output-dir ./outputs/opensearch_vl_8b \
    --start 0 --end 1000

# MoE Qwen3-VL-30B-A3B with 4-way model parallel (auto-applies the scatter dtype patch)
python opensearch_vl/run_infer.py --model 30b-a3b --gpus 0,1,2,3 \
    --checkpoint /path/to/OpenSearch-VL-30B-A3B \
    --data-path  /path/to/benchmark.parquet \
    --output-dir ./outputs/opensearch_vl_30b_a3b

# Claude Opus 4.5 (CLAUDE_API_HOST / _USER / _KEY required)
python opensearch_vl/run_infer.py --model claude \
    --data-path  /path/to/benchmark.parquet \
    --output-dir ./outputs/claude_opus

The shell wrapper opensearch_vl/run_infer.sh reads the same parameters from environment variables (MODEL, GPUS, DATA_PATH, OUTPUT_DIR, LIMIT, CATEGORY, ...) for one-line invocations.

Benchmark evaluation

eval_with_gpt4o.py consumes the trajectory directory produced above and calls a GPT-4o-class judge to compute per-sample correctness using the VDR-Bench evaluation prompt:

python opensearch_vl/eval_with_gpt4o.py \
    --traj_dir    ./outputs/opensearch_vl_8b/bc_vl_level1 \
    --benchmark   bc_vl            # one of: hle | bc_vl | vdr
    --max_workers 20

--answer_file is required for VDR-Bench (pass the .parquet with id / answer columns).

run_eval.sh is a thin driver that chains the five reported evaluations (BrowseComp-VL L1, BrowseComp-VL L2, HLE, VDR-Bench testmini × 2 models). Configure trajectory directories via env variables (TRAJ_BC_VL_LEVEL1, TRAJ_BC_VL_LEVEL2, TRAJ_HLE, TRAJ_VDR_PRIMARY, TRAJ_VDR_SECONDARY, VDR_ANSWER_PARQUET) and run:

bash opensearch_vl/run_eval.sh --workers 20

Full inference details: opensearch_vl/README.md.

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🚧 TODO

• Release OpenSearch-VL-{8B, 30B-A3B, 32B} checkpoints on the HuggingFace collection.
• Release SearchVL-SFT-36k and SearchVL-RL-8k datasets (full bundle + image assets).
• Release the data curation pipeline (Wikipedia path sampling, fuzzy entity rewriting, source-anchor visual grounding) as a standalone toolkit.
• Public demo for interactive multi-turn deep-research rollouts.

All artifacts have entered the final approval stage. Stay tuned — once the approval process is complete, we will release them ASAP.

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🙌 Acknowledgements

This repository bundles and builds on several outstanding open-source projects; each sub-directory retains its upstream LICENSE:

• LLaMA-Factory — SFT trainer and CLI (code/SFT/).
• rLLM and verl — agentic RL framework (code/RL/rllm/).
• Megatron-LM and mbridge — model-parallel backend (code/RL/Megatron-LM/, code/RL/mbridge/).
• sglang — async rollout engine.
• Qwen-VL — base VLM checkpoints.
• We also thank the authors of Search-R1 and Vision-DeepResearch whose ideas inspired our multi-turn search-augmented RL formulation.

Project-specific additions are released under the root LICENSE (Apache 2.0).

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