WebShaper turns data generation for web agents into a set-theory science

 LLM-powered web agents nibble at problems once reserved for human researchers, but they’re starving for the one thing that matters—clean, diverse question-answer trajectories. Most teams still scrape pages first and dream up queries later, a workflow that tangles reasoning paths and spawns hallucinated answers. Alibaba’s Tongyi Lab says it has a better recipe: WebShaper, a “formalization-driven” data factory that starts with mathematics, not HTML. 

From ad-hoc scraping to knowledge projections

At the heart of WebShaper is a set-theoretic vocabulary called Knowledge Projections (KP): each KP is the set of entities linked by a single relation ( bornIn, playsFor, etc.). Two operations—union and intersection—let the authors compose arbitrarily deep queries and guarantee that every synthetic problem has a fully specified reasoning graph. The formal spec acts as a skeleton; only then does an agentic “Expander” venture onto the open web to fetch evidence that satisfies each KP node. 

A multi-step agent that grows harder questions

WebShaper starts with 18 k seed Q&A pairs distilled from an offline Wikipedia crawl, then pushes them through n-step expansions. At each step, the Expander retrieves fresh pages, validates candidates, and rewrites the KP tree into a tougher query—controlling complexity like a curriculum designer rather than a random crawler. 

Why it matters

• Broader coverage – formal specs explore search patterns unconstrained by whatever a scraper happened to collect.

• Structural consistency – answers align with the reasoning graph, slashing mismatched Q–A pairs.

• Dial-a-difficulty – KP depth and branching let teams script “easy” or “nightmare” tasks on demand. 

State-of-the-art results with leaner data

Training a 72 B agent on the new dataset catapulted WebShaper-72B to 60.2 % on GAIA’s information-seeking subset, beating Claude-Sonnet, GPT-4.1 and Gemini 2.5 Pro when all models shared the same two browsing tools. Even the 32 B version tops WebDancer and SimpleDR. 

Model	GAIA ↑	Notes
WebShaper-72B	60.2 %	new SOTA
Claude-Sonnet *	58.3 %	proprietary
WebShaper-32B	55.4 %	open
WebSailor	55.3 %	open
GPT-4.1 *	48.5 %	proprietary

* scores reported using the same browsing APIs

Because the formal spec eliminates redundant retrieval, WebShaper needs ~42 % of the tokens consumed by earlier pipelines such as WebDancer, yet still outperforms them on WebWalkerQA. 

Open kits for builders

All resources are public:

• Dataset: on Hugging Face and ModelScope

• Code: GitHub/Alibaba-NLP/WebAgent, including the Expander scripts

• Checkpoints: 32 B & 72 B SFT models ready for RL fine-tuning 

The bigger picture

WebShaper reframes web-agent training as data geometry rather than brute-force scraping. By baking reasoning patterns into the data itself, it closes the loop between question design and answer verification—an approach that could spill over into multi-hop RAG, legal search and even agentic code auditors. The message is simple: if you can formalize the hunt, you can synthesize the bounty.

Paper link: arXiv 2507.15061 (PDF)

WebSailor charts an open-source course to super-human web reasoning

 For the past year, open-source web agents have looked like dinghies chasing aircraft carriers: even 70-billion-parameter models scraped single-digit accuracy on BrowseComp-en, the field’s toughest information-seeking benchmark, while closed systems such as DeepResearch and Grok-3 cruised far ahead. Tongyi Lab, Alibaba’s applied-AI skunkworks, says it has all but closed that gap with WebSailor, a post-training recipe that rewires large language models to “think like uncertainty-slayers.” 

Turning the web into a maze on purpose

At the heart of WebSailor is SailorFog-QA, a synthetic dataset that bombards the model with “Level-3” problems—questions whose answers hide behind tangled entity graphs and deliberately obfuscated clues (“a musician later honored in the early 21st century,” “a chronology that ends the same year a late-antique poet died”). Random walks over real web pages build those graphs; masking, vagueness and partial names turn each query into a fog bank the agent must burn off through multi-step reasoning. 

DUPO: reinforcement learning that isn’t painfully slow

Tool-using agents learn painfully slowly because every step calls a browser, but Tongyi Lab’s Duplicating Sampling Policy Optimization (DUPO) makes each RL batch pull double duty: one pass samples harder trajectories, the next re-samples mid-episode to squeeze more signal from sparse rewards. A small rejection-sampling fine-tuning (RFT) “cold start” of just 2 k expert traces primes the model so DUPO has something to optimize. 

Four sizes, one giant leap

WebSailor comes in 3B, 7B, 32B and 72B flavors. Even the 7-billion-parameter version hits 6.7 % pass@1 on BrowseComp-en, trouncing agents built on 32 B backbones that manage barely 2 – 3 %. The 32 B and 72 B models push further, outscoring open-source peers on BrowseComp-en/zh, GAIA and XBench and edging past proprietary offerings like Grok-3 and Doubao-Search when those systems add browsing tools. 

Why it matters

• Democratizing deep search. BrowseComp-level tasks—ask a question, navigate dozen-plus pages, synthesize an answer—are what corporate knowledge-bases and vertical search startups need. WebSailor shows you no longer need a closed-source giant to play.

• A recipe, not a model. The CPT + HCF routine, uncertainty-first data and DUPO optimizer are architecture-agnostic; any ReAct-style agent with tool APIs can adopt them.

• Downward compatibility. Despite training only on headache-grade puzzles, WebSailor’s 72 B model scores >90 % pass@1 on the single-hop SimpleQA benchmark, proving that hard-first curricula don’t break easy tasks. 

Open weights, open benchmark

Code, data-generation scripts and checkpoints live in Tongyi Lab’s GitHub repo, alongside a dockerized evaluator so outside teams can reproduce—or dispute—the numbers. 

With WebSailor, the open-source fleet finally has a flagship capable of keeping proprietary juggernauts in sight. The real question now: how long before someone splices SailorFog-style data and DUPO into a general-purpose agent that can shop, schedule and navigate enterprise wikis with the same super-human calm?

Paper link: arXiv 2507.02592         (PDF)