stitch

A framework-agnostic protocol for disaggregated reinforcement learning of LLMs.

When training and rollout generation run on separate machines, the rollout servers need a way to pick up new weights as the trainer produces them, and each rollout request needs to know which weight version it was served with. stitch provides the protocol and glue for that. Trainers publish immutable, versioned weight artifacts to a shared "bulletin board" directory, rollout servers sync to a requested version, and completion requests declare which versions they will accept.

stitch is unopinionated about algorithm/training framework but is strongly opinionated about supporting workloads that are:

• async-first
• agentic-first
• elastic rollout compute

How it works

1. After an optimizer step, the trainer writes weight artifacts (e.g. sparse deltas) for version v to the bulletin board, publishes a manifest.json describing them, then advances latest.json.
2. A sidecar in front of each rollout server watches the board. When a request arrives pinned to version v (via a weight_version constraint in the request body), the sidecar applies versions in order until it reaches v, then proxies the request to the engine.
3. Responses carry the version they were served with, and a server that hasn't caught up returns 409 WeightVersionNotReady so the trainer can retry.

Layout

• src/stitch/protocol.py: Wire protocol. Version manifests, artifacts, request policies, the latest.json pointer, version-namespaced cache keys.
• src/stitch/bulletin.py: Bulletin board storage (filesystem-backed, with a pluggable refresh hook for remote volumes).
• src/stitch/sync.py: Sync state machine that drives a server from its current version to a target, with quiesce (drain, then apply) and in_place (pause/apply/continue) commit modes.
• src/stitch/servers/sglang.py: HTTP sidecar that adds version semantics to an SGLang server.
• src/stitch/engines/sglang.py: SGLang prepare/commit adapter using /update_weights_from_disk.
• src/stitch/trainers/slime.py: Hooks for slime that publish sparse-delta versions from training ranks.
• src/stitch/providers/modal.py: Modal helpers for Volume commit/reload and Flash container discovery.
• cookbook/: End-to-end examples.
  • slime_disagg/: SLIME plus a stitch-managed Modal Flash/SGLang pool.
  • standalone_rollouts/: standalone Modal/SGLang rollout provider with a hot-load API shim.

The core package has no required dependencies; extras pull in what each adapter needs (modal, sglang, slime).

Adding adapters

Trainer adapters should publish canonical Hugging Face tensor names so engine adapters stay trainer-agnostic. Engine adapters implement the same prepare/commit contract as the SGLang one; the request protocol doesn't change.

Development

uv run pytest