WebRL
Training pipeline that evolves web tasks from prior attempts and uses an outcome reward model to refine browser policies.
AI-assisted editorial review against a primary source. This is a catalog annotation, not an independent reproduction or endorsement.
What is this resource?
- Resource type
- Frameworks
- Environment
- web
- Topics
- reinforcement-learning, web-navigation, data-generation
- Record publication date
- Not recorded
- Evidence retrieved
- 2026-10-17T02:02:06Z
- Resource ID
- github:thudm/webrl
What licenses are recorded?
Code, training data, weights, and documents can have different terms. A blank declaration means unknown, not unrestricted use.
- Code
- Not recorded
- Data
- Not recorded
- Document
- Not recorded
- Weights
- Not recorded
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Scope and limitations
Training code and checkpoint links are released. No root code-license declaration was found in the inspected repository metadata; bundled dependency licenses do not establish a license for the whole project. Weights have separate Hub terms.
Related resources
- documented by: WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning
Primary evidence and verification
Source retrieval is separate from experiment reproduction. Use the original publication or publisher page when citing claims about the resource.
- editorial primary review
Retrieved 2026-10-17T02:02:06Z. Supports: name, kind, summary, environments, topics, scope, relations.
Content fingerprint
eb217b96c54c3c2ccfd3a27255c6fd740df40c2d6743ae811858339bf7f740b0 - editorial primary review
Retrieved 2026-10-17T02:02:05Z. Supports: identity.
Content fingerprint
42496cf0cff06a7f9e794f4313a10a670bc48df9909ec6d57763cb902bb229ea - editorial primary review
Retrieved 2026-10-17T02:02:05Z. Supports: release_status.
Content fingerprint
bb1683c6fb5ff658c89f0ebf0f8baf8026bb703840e174c9e46b223c85a16856
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