What Orchard Is
Orchard is not an agent framework. It is the *environment layer* for agent training — a reusable service that solves the engineering pain of running tens of thousands of parallel agent experiments without standing up an isolated sandbox for every single run.
Core Architecture: Orchard Env
- Kubernetes-native environment service + Python SDK that provisions thousands of isolated containers on demand.
- Exposes generic capabilities over HTTP: sandbox lifecycle, command execution, file I/O, network control, agent integration.
- Harness-neutral: the same Env supports SFT trajectory distillation, RL rollouts, and evaluation task switching — no rebuild needed when the task changes.
- In the arXiv comparison table, Orchard Env is the only open-source entry that fits the "narrow, independent, reusable" definition for a thin environment service.
- Cost: ~0.47x Daytona per 2-vCPU/8-GiB sandbox; ~0.10x with spot instances.
- Base model: Qwen3.5-35B-A3B.
- 107K distilled trajectories (from MiniMax-M2.5 and Qwen3.5-397B) for *credit-assignment SFT* — only productive segments from unresolved trajectories.
- + Balanced Adaptive Rollout (BAR) RL.
- SWE-bench Verified: 61.4% → 69.1% (+BAR) → 69.7% (+dense reward) → 73% with a 4B value-model reranker — approaching closed-source systems ~10x larger.
- 4B vision-language model acting as a browser agent.
- Only 400 distilled trajectories + 2,200 open-ended tasks.
- WebVoyager 74.1%, Online-Mind2Web 67.0%, DeepShop 64.0%, average 68.4% — open-source SOTA, competitive with OpenAI/Google CUA.
- 30B-A3B personal-assistant agent, starting from 200 synthetic tasks.
- pass@3 on Claw-Eval: 59.6%; with the ZeroClaw harness: 73.9%.
- Training runs inside real deployment harnesses (ReACT, ZeroClaw, OpenClaw, Codex), not a "simplified loop".
- Under the Codex harness: 18.6% → 51.5%.
- Updated in July. Traditional RL stacks train on simplified harnesses; switching harnesses at deployment collapses performance.
- OpenForge adds a lightweight proxy that records calls from the real deployment harness and reconstructs RL samples for any RL codebase (e.g., veRL).
- Orchard Env spins up matching containers for rollouts in parallel.
- Results:
- OpenForge-GUI 8B: OSWorld-Verified 37.7, WebVoyager 72.3.
- OpenForge-Claw 30B-A3B: QwenClawBench 33.7, MCPAtlas 28.1.
- Code: https://github.com/microsoft/Orchard
- Paper: https://arxiv.org/html/2605.15040
Three Training Recipes Sharing One Stack
Orchard-SWE
Orchard-GUI
Orchard-Claw
OpenForge RL: Bridging the Train-Deploy Gap
Why It Matters
The signal isn't in the scores — it's in the taxonomy. Orchard decouples "environment" from the training stack as a reusable service. That single abstraction lets one infrastructure layer serve SWE, GUI, and Claw domains simultaneously.
The next phase of agent training is unlikely to be "bigger models + more trajectories". It is more likely to be cheaper, standardized environment pools + cross-harness RL. For teams building agent training frameworks — whether on LangGraph, ReAct, or in-house runtimes — the design lesson is clear: treat environments as services, not components, and make training a swappable backend.