TorchRL call for contributions
Last reviewed: August 2026
This is a living contributor hub for TorchRL's current priorities. It is not a release schedule, and the focus areas may evolve as designs land and project needs change.
How to contribute
- Pick an open issue below and read its latest discussion.
- Check the assignees and recent comments before starting.
- Comment on the issue to agree on scope, especially for RFCs and assigned work.
- Follow the repository's contribution guide for implementation, tests, documentation, and pull requests.
If you would like to propose work that is not listed here, open a dedicated issue first. New entries in this hub must point to an open issue with a clear motivation and enough scope for contributors to discuss or implement.
Near-term focus
Post-training interoperability
Make TorchRL's post-training components easier to adopt from existing ecosystems and training loops.
Scalable asynchronous collection and inference
Improve throughput and ergonomics for large asynchronous environment fleets and accelerator-backed policies.
Replay-buffer composition and update safety
Separate sampling concerns cleanly and make delayed mutable updates safe under storage reuse.
Monitoring and consistent runtime APIs
Improve observability and make configuration consistent across collectors, evaluators, replay buffers, and inference services.
Ways to contribute now
Implementation opportunity
Design and discussion opportunities
These RFCs are open for concrete API feedback, use cases, and implementation trade-offs:
Coordinate before coding
The post-training, AsyncEnvPool, and replay-buffer workstreams above are active, larger efforts with current ownership. Focused prototypes, benchmarks, tests, documentation, and scoped implementation help may be useful, but please agree on the boundary in the linked issue first.
Keeping this page current
- Every listed item must have a dedicated open issue.
- Completed or deprioritized items should be removed instead of kept as a historical archive.
- Closed issues and release notes are the source of truth for completed work.
Thanks for contributing to TorchRL!
TorchRL call for contributions
How to contribute
If you would like to propose work that is not listed here, open a dedicated issue first. New entries in this hub must point to an open issue with a clear motivation and enough scope for contributors to discuss or implement.
Near-term focus
Post-training interoperability
Make TorchRL's post-training components easier to adopt from existing ecosystems and training loops.
Scalable asynchronous collection and inference
Improve throughput and ergonomics for large asynchronous environment fleets and accelerator-backed policies.
Replay-buffer composition and update safety
Separate sampling concerns cleanly and make delayed mutable updates safe under storage reuse.
Monitoring and consistent runtime APIs
Improve observability and make configuration consistent across collectors, evaluators, replay buffers, and inference services.
Ways to contribute now
Implementation opportunity
Design and discussion opportunities
These RFCs are open for concrete API feedback, use cases, and implementation trade-offs:
Coordinate before coding
The post-training, AsyncEnvPool, and replay-buffer workstreams above are active, larger efforts with current ownership. Focused prototypes, benchmarks, tests, documentation, and scoped implementation help may be useful, but please agree on the boundary in the linked issue first.
Keeping this page current
Thanks for contributing to TorchRL!