Lego-RL-Qwen3.5-35B-A3B · OpenCode · 200K
Qwen3.5-35B-A3B trained with online RL inside the unmodified OpenCode harness, at 200K context, on 2,699 real repository issues whose own test suites produce the reward.
SWE-bench Verified: 57.2 → 66.6 (+9.4) — no reward model, no reference-patch similarity, no harness rewrite.
This checkpoint is the OpenCode production run of
Lego-RL (
Faithful · Reliable · Observable), released as training step
50. The OpenHands SDK counterpart is
Lego-X/qwen3_5_35b_a3b_base_ohsdk_200k_rl; the Claude Code run is
Lego-X/qwen3_5_35b_a3b_base_cc_200k_rl.
The agent solves a real issue in a real repository inside a fresh sandbox, the task's own verifier suite decides {0, 1}, and the trajectory the harness actually produced — token ids, masks, log-probs and MoE expert routes captured inside the serving path — becomes the gradient step.
Why harness-native training
The scaffold is part of the environment, not the policy. The same weights score very differently depending on which harness runs them, so training under a rewritten control flow optimizes for a deployment you never ship:
| Model | OpenHands SDK | Claude Code | OpenCode |
|---|
| Qwen3.5-35B-A3B (starting point) | 64.0 | 62.4 | 57.2 |
| Qwen3.6-35B-A3B (next-gen base) | 67.4 | 63.4 | 60.6 |
| KAT-Coder-V2.5-Dev (post-trained Qwen3.6) | 67.0 | 66.8 | 64.8 |
| Lego-RL-Qwen3.5-35B-A3B | 70.4 | 68.2 | 66.6 |
SWE-bench Verified (%), one shared protocol: temperature 0.7, 200 turns, 200K context.
Each Lego-RL column is a separate run trained in that harness from the same starting checkpoint, the same 2,699 tasks and the same 3 epochs. This repository is the OpenCode run (66.6). Across the three harnesses RL adds +6.4 / +5.8 / +9.4.
Quick start
1. Serve with vLLM
1vllm serve Lego-X/qwen3_5_35b_a3b_base_oc_200k_rl \
2 --served-model-name vllm_model \
3 --tensor-parallel-size 4 \
4 --enable-expert-parallel \
5 --max-model-len 262144 \
6 --gpu-memory-utilization 0.9 \
7 --enable-chunked-prefill --enable-prefix-caching \
8 --dtype bfloat16 \
9 --enable-auto-tool-choice \
10 --tool-call-parser qwen3_coder \
11 --host 0.0.0.0 --port 8000
[!IMPORTANT]
--tool-call-parser qwen3_coder is not optional. The model was rolled out and trained with this parser; serving it behind hermes (or any other parser) silently degrades tool-call formatting.
2. Drive it with OpenCode
Point OpenCode at the vLLM endpoint as an OpenAI-compatible provider, and give it a repository workspace plus a 200-turn / 200K budget. The RL policy learned to spend turns; short turn caps systematically truncate the second half of its trajectories and cost most of the gain.
3. Reproduce the evaluation
1git clone https://github.com/LegoX/Lego-RL.git && cd Lego-RL
2bash scripts/setup_env.sh
3cp scripts/eval/_template.env scripts/eval/configs/my_eval.env # set MODEL_PATH, DATASET_PATH, kubeconfig
4bash scripts/eval/eval.sh scripts/eval/configs/my_eval.env
Sandboxed execution and verifier rewards come from
Harbor; see the
evaluation docs.
Training
| |
|---|
| Starting checkpoint | Qwen/Qwen3.5-35B-A3B (sparse MoE, 256 experts, 8 active) |
| Harness | OpenCode, unmodified — a thin adapter, not a fork |
| Released step | global_step_50 |
| Tasks | Lego-X/Lego-RL-2699 — 2,699 real repository issues, converted from GAIR/OpenSWE |
| Reward | each task's own test suite, run in a fresh sandbox: {0, 1}. No reward model, no patch similarity, no LLM judge |
| Algorithm | GSPO (sequence-level surrogate), group-relative advantage over G = 8 rollouts per task |
| Batch | 64 prompts × 8 responses = 512 trials/step; 3 epochs = 126 steps |
| Optimizer | lr 1e-6 constant, KL loss 1e-3 (low-var), clip [3e-4, 4e-4], rollout temperature 1.0 |
| Context | 200K, 200 turns |
| Backend | VeOmni FSDP, Ulysses SP = 8, R3 rollout routing replay; fully-async with partial rollout (staleness 1) |
| Hardware | 3 nodes × 8 GPUs (2 training + 1 rollout) |
The training set is disjoint from SWE-bench Verified at both the repository and the instance level.
Intended use and limitations
Use it as an agent policy, not as a chat model: it was optimized inside a harness that hands it a repository, a shell, and file-editing tools.
- Harness. Trained in OpenCode. A separate policy is released for OpenHands SDK and Claude Code; each does best in the harness it was trained in.
- Budget. 200K context and 200 turns. Short budgets truncate it.
- Domain. Python-heavy repository issue-resolution, in the SWE-bench/OpenSWE distribution.
- Inherited base behavior. Safety, multilingual and general-knowledge behavior come from Qwen3.5-35B-A3B and were not targeted by this RL.
- Sandbox it. The policy writes files and executes shell commands on purpose. Run it in a container.
Related work in the LegoX series
Acknowledgement
Built on
verl (trainer + rollout) and
Harbor (sandboxed execution + verifier reward), with
OpenCode as the harness.
Citation
1@misc{du2026legorlharnessnativereinforcementlearning,
2 title={LEGO-RL: Harness-Native Reinforcement Learning for Coding Agents},
3 author={Yiming Du and Yuxin Jiang and Tao Yuan and Jianbo Dai and Shaowei Wang and Jierun Chen and Chaofan Tao and Xianzhi Yu and Lifeng Shang and Kam-Fai Wong and Xiaohui Li and Haoli Bai},
4 year={2026},
5 eprint={2608.17393},
6 archivePrefix={arXiv},
7 primaryClass={cs.AI},
8 url={https://arxiv.org/abs/2608.17393},
9}
