Update README.md

README.md

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 The predominant approach for training web navigation agents is to gather human demonstrations for a set of popular websites and hand-written tasks, but it is becoming clear that human data is an inefficient resource. We develop a pipeline to facilitate internet-scale training for agents without laborious human annotations. In the first stage, an LLM annotates 150k sites with agentic tasks. In the next stage, LLM agents complete tasks and produce trajectories. In the final stage, an LLM filters trajectories by judging their success. Language models are powerful data curation tools, identifying harmful content with an accuracy of 97%, judging successful trajectories with an accuracy of 82.6%, and producing effective data. We train agents based on *Qwen 3 1.7B* that are competitive with frontier LLMs as web agents, while being smaller and faster. Our top agent reaches a success rate of 56.9%, outperforming the data collection policy *Qwen 3 235B*, a 235 times larger *Llama 4 Maverick*, and reaching 94.7% of the performance of *Gemini 2.5 Flash*. We are releasing code, models and data at: [data-for-agents.github.io](https://data-for-agents.github.io).
 
-[website](https://data-for-agents.github.io)    |    [paper](https://arxiv.org/abs/2502.06776)    |    [data](https://huggingface.co/datasets/data-for-agents/insta-150k-v3)
+[website](https://data-for-agents.github.io)    |    [paper](https://arxiv.org/abs/2502.06776)    |    [code](https://github.com/data-for-agents/insta)    |    [data](https://huggingface.co/datasets/data-for-agents/insta-150k-v3)
 
 ## Section 4 - Internet-Scale Task Generation
 
-![Task Generation Overview](https://data-for-agents.github.io/static/images/task_generation_overview.png)
+![Task Generation Overview](https://data-for-agents.github.io/static/images/pipeline_overview.png)
 
-**Task proposal and filtering for 150k live websites.** Starting from 1,000,000 websites, we employ a pretrained language model that marks sites as safe/unsafe for annotation, and assigns a realistic task that a hypothetical user might want to accomplish on each site. The task proposer rejects 85% of websites from the pipeline, resulting in 150k safe websites annotated with realistic tasks.
+**Overview of the InSTA pipeline.** Our work unlocks a dynamic internet-scale environment that allows training small models that match top industry LLMs as agents, on a fraction of the budget. Starting from the top 1M sites on the internet, we efficiently annotate 150k sites with challenging agentic tasks, and release the entire pipeline, including code, models and an official huggingface dataset.
 
-## Section 5 - Internet-Scale Agents
+### Exploring The Tasks
+
+![Task Wordcloud](https://data-for-agents.github.io/static/images/wordcloud.png)
+
+**Most frequent words in our tasks.** This wordcloud shows the top 500 most frequent words in tasks from the training set of our official huggingface dataset. The size of each word corresponds to its frequency in the dataset. Our tasks span diverse categories and lexicon.
+
+## Section 5 - Internet-Scale Environment
 
 ![Environment Overview](https://data-for-agents.github.io/static/images/environment_overview.png)
 
-**Automatic evaluation for agents with language model judges.** Building on the large and diverse set of tasks generated by the pipeline, we employ pretrained language models to attempt and evaluate web navigation tasks. We dispatch language model agents to perform tasks by making calls to the Playwright API. We then employ language model judges to evaluate rollouts from agents.
+**Unlocking a dynamic internet-scale environment.** Building on the large and diverse set of tasks, we employ pretrained language models to attempt and evaluate tasks. We run language model agents to perform tasks using the Playwright API. We then employ language model judges to evaluate trajectories.
 
 ## Section 6 - Training Agents
 
 ![Training Results](https://data-for-agents.github.io/static/images/training-results.png)
 
-**Training agents with internet-scale data.** In data-limited settings derived from Mind2Web and WebLINX (left plot), we improve Step Accuracy by up to +89.5% and +122.1% respectively for agents trained on mixtures of data from our pipeline, and human data. When training agents with all available human data from these benchmarks (right plot), agents trained on existing human data struggle to generalize to diverse real sites, and adding our data improves their generalization by +149.0% for WebLINX and +156.3% for Mind2Web.
+**InSTA unlocks great potential in small models.** We train agents based on *Qwen 3 1.7B* using trajectories produced by a *Qwen 3 235B* data collection policy, and optionally filtered by a *Qwen 3 235B* judge (see Judge Filtered vs. Uniformly Sampled). We report success rates on a test set of 3,000 held-out websites and tasks. Before training, *Qwen 3 1.7B* has a zero-shot success rate of 11.5% according to a *Qwen 3 235B* judge, and we improve this by +45.3% absolute percentage points. Our top checkpoint outperforms the *Qwen 3 235B* data collection policy, and *Llama 4 Maverick*, a frontier LLM with 400B parameters, for which our model is 235 times smaller. Notably, filtering with a *Qwen 3 235B* judge leads to agents that improve according to independent secondary judges, including *Gemini 2.5 Flash*, *Llama 4 Maverick*, and *Gpt 4.1 Nano*, suggesting it generalizes well.
 
 ## Citing Us