Microsoft AI Releases Fara-7B: An Efficient Agentic Model for Computer Use

How can we safely let an AI agent deal with actual internet duties like reserving, looking out, and type filling immediately on our personal units with out sending all the things to the cloud? Microsoft Research has launched Fara-7B, a 7 billion parameter agentic small language model designed particularly for laptop use. It is an open weight Computer Use Agent that runs from screenshots, predicts mouse and keyboard actions, and is sufficiently small to execute on a single person system, which reduces latency and retains searching information native.

From Chatbots to Computer Use Agents

Conventional chat oriented LLMs return textual content. Computer Use Agents comparable to Fara-7B as a substitute management the browser or desktop person interface to finish duties like filling varieties, reserving journey, or evaluating costs. They understand the display, cause concerning the web page format, then emit low degree actions comparable to click on, scroll, kind, web_search, or visit_url.

Many present programs depend on massive multimodal fashions wrapped in complicated scaffolding that parses accessibility bushes and orchestrates a number of instruments. This will increase latency and infrequently requires server facet deployment. Fara-7B compresses the conduct of such multi agent programs right into a single multimodal decoder solely mannequin constructed on Qwen2.5-VL-7B. It consumes browser screenshots and textual content context, then immediately outputs thought textual content adopted by a instrument name with grounded arguments comparable to coordinates, textual content, or URLs.

FaraGen, Synthetic Trajectories for Web Interaction

The key bottleneck for Computer Use Agents is information. High high quality logs of human internet interplay with multi step actions are uncommon and costly to gather. The Fara challenge introduces FaraGen, an artificial information engine that generates and filters internet trajectories on dwell websites.

FaraGen makes use of a 3 stage pipeline. Task Proposal begins from seed URLs drawn from public corpora comparable to ClueWeb22 and Tranco, that are categorized into domains like e commerce, journey, leisure, or boards. Large language fashions convert every URL into reasonable duties that customers would possibly try on that web page, for instance reserving particular film tickets or making a procuring listing with constraints on critiques and supplies. Tasks have to be achievable with out login or paywall, absolutely specified, helpful, and routinely verifiable.

Task Solving runs a multi agent system based mostly on Magentic-One and Magentic-UI. An Orchestrator agent plans the excessive degree technique and retains a ledger of job state. A WebSurfer agent receives accessibility bushes and Set-of-Marks screenshots, then emits browser actions via Playwright, comparable to click on, kind, scroll, visit_url, or web_search. A UserSimulator agent provides observe up directions when the duty wants clarification.

Trajectory Verification makes use of three LLM based mostly verifiers. An Alignment Verifier checks that the actions and last reply match the duty intent. A Rubric Verifier generates a rubric of subgoals and scores partial completion. A Multimodal Verifier inspects screenshots plus the ultimate reply to catch hallucinations and ensure that seen proof helps success. These verifiers agree with human labels on 83.3 % of instances, with reported false constructive and false unfavourable charges round 17 to 18 %.

After filtering, FaraGen yields 145,603 trajectories with 1,010,797 steps over 70,117 distinctive domains. The trajectories vary from 3 to 84 steps, with a mean of 6.9 steps and about 0.5 distinctive domains per trajectory, which signifies that many duties contain websites not seen elsewhere within the dataset. Generating information with premium fashions comparable to GPT-5 and o3 prices roughly 1 greenback per verified trajectory.

Model Architecture

Fara-7B is a multimodal decoder solely mannequin that makes use of Qwen2.5-VL-7B as the bottom. It takes as enter a person purpose, the most recent screenshots from the browser, and the total historical past of earlier ideas and actions. The context window is 128,000 tokens. At every step the mannequin first generates a series of thought describing the present state and the plan, then outputs a instrument name that specifies the subsequent motion and its arguments.

The instrument area matches the Magentic-UI computer_use interface. It consists of key, kind, mouse_move, left_click, scroll, visit_url, web_search, history_back, pause_and_memorize_fact, wait, and terminate. Coordinates are predicted immediately as pixel positions on the screenshot, which permits the mannequin to function with out entry to the accessibility tree at inference time.

Training makes use of supervised finetuning over roughly 1.8 million samples that blend a number of information sources. These embody the FaraGen trajectories damaged into observe assume act steps, grounding and UI localization duties, screenshot based mostly visible query answering and captioning, and security and refusal datasets.

Benchmarks and Efficiency

Microsoft evaluates Fara-7B on 4 dwell internet benchmarks: WebVoyager, Online-Mind2Web, DeepShop, and the brand new WebTailBench, which focuses on below represented segments comparable to restaurant reservations, job functions, actual property search, comparability procuring, and multi web site compositional duties.

On these benchmarks, Fara-7B achieves 73.5 % success on WebVoyager, 34.1 % on Online-Mind2Web, 26.2 % on DeepShop, and 38.4 % on WebTailBench. This outperforms the 7B Computer Use Agent baseline UI-TARS-1.5-7B, which scores 66.4, 31.3, 11.6, and 19.5 respectively, and compares favorably to bigger programs like OpenAI computer-use-preview and SoM Agent configurations constructed on GPT-4o.

On WebVoyager, Fara-7B makes use of on common 124,000 enter tokens and 1,100 output tokens per job, with about 16.5 actions. Using market token costs, the analysis workforce estimate a mean value of 0.025 {dollars} per job, versus round 0.30 {dollars} for SoM brokers backed by proprietary reasoning fashions comparable to GPT-5 and o3. Fara-7B makes use of an analogous variety of enter tokens however about one tenth the output tokens of those SoM brokers.

Key Takeaways

• Fara-7B is a 7B parameter, open weight Computer Use Agent constructed on Qwen2.5-VL-7B that operates immediately from screenshots and textual content, then outputs grounded actions comparable to clicks, typing and navigation, with out counting on accessibility bushes at inference time.
• The mannequin is skilled with 145,603 verified browser trajectories and 1,010,797 steps generated by the FaraGen pipeline, which makes use of multi agent job proposal, fixing, and LLM based mostly verification on dwell web sites throughout 70,117 domains.
• Fara-7B achieves 73.5 % success on WebVoyager, 34.1 % on Online-Mind2Web, 26.2 % on DeepShop, and 38.4 % on WebTailBench, enhancing considerably over the 7B UI-TARS-1.5 baseline on all 4 benchmarks.
• On WebVoyager, Fara-7B makes use of about 124,000 enter tokens and 1,100 output tokens per job, with a mean of 16.5 actions, yielding an estimated value of round 0.025 {dollars} per job, which is round an order of magnitude cheaper in output token utilization than SoM brokers backed by GPT 5 class fashions.

Editorial Notes

Fara-7B is a helpful step towards sensible Computer Use Agents that may run on native {hardware} with decrease inference value whereas preserving privateness. The mixture of Qwen2.5 VL 7B, FaraGen artificial trajectories and WebTailBench offers a transparent and effectively instrumented path from multi agent information technology to a single compact mannequin that matches or exceeds bigger programs on key benchmarks whereas implementing Critical Point and refusal safeguards.

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