---
license: apache-2.0
base_model: Qwen/Qwen3.6-27B
tags:
- lora
- peft
- swe-bench
- code-repair
- trace-pipeline
---

# Qwen3.6-27B TRACE LoRA — Target Capability: `semantic-logic-precision`

GRPO-trained LoRA adapters on top of `Qwen/Qwen3.6-27B`, produced by the
**TRACE** (Test-time Reinforcement Learning Adaptation via Capability
Environments) pipeline.

## Target capability

`semantic-logic-precision` was identified via open-ended capability mining
(phase 2 of TRACE): Qwen3.6 itself read the 123 failed SWE-bench Verified
trajectories from its own baseline run and proposed a 5-category taxonomy of
its weaknesses. `semantic-logic-precision` was the largest category at **61%
of failures**:

> Implements a logically incorrect algorithm — misunderstanding domain
> protocols, mathematical rules, or language semantics. Code is
> syntactically valid but solves the wrong problem.

## Training data

Synthetic single-turn SEARCH/REPLACE scenarios (phase 3 v4 + v5 mutation):

| Scenario | Base success rate | Description |
|---|---:|---|
| `pid-autotuner-ku-formula-fix` | 0.30 | Wrong Ziegler-Nichols Ku formula (extra `* 2` in denominator) |
| `pid-autotuner-ku-formula-fix_mut1_1` | 0.50 | Same bug, surface code disguised: identifiers renamed, decoy edit sites, misleading "logic is correct" comment |

## Training setup

- **Trainer**: GRPO with rank-16 LoRA (`q_proj, k_proj, v_proj, o_proj`)
- **Group size**: 8 rollouts per group (DS-retry on constant-reward groups)
- **Inference backend**: vLLM TP=4 with hot-reload LoRA after each policy step
- **Hardware**: 4×H100 80GB
- **Total iterations**: 130 (saturated around iter ~30-50)
- **Per-iter wall time**: ~70-100 sec

## Checkpoints

| Subfolder | When taken | Notes |
|---|---|---|
| `iter_30/` | Mid-learning | Generalizable signal, low overfit risk |
| `iter_50/` | Recommended | Best balance of learning vs overfit |
| `iter_100/` | Near-saturation | High training-set performance, possibly overfit |
| `iter_130/` | Final | Peak training reward, may not transfer better than `iter_50` |

## Loading

### vLLM (production / eval)

```bash
python3 -c "
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id='ScalingIntelligence/qwen3.6-trace-semantic-logic-precision-v1',
    allow_patterns='iter_50/*',
    local_dir='/tmp/trace_lora',
)
"

curl -X POST http://localhost:8000/v1/load_lora_adapter \
  -H 'Content-Type: application/json' \
  -d '{"lora_name": "trace_iter50", "lora_path": "/tmp/trace_lora/iter_50"}'
```

### PEFT + transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3.6-27B", torch_dtype=torch.bfloat16, device_map="auto"
)
model = PeftModel.from_pretrained(
    base,
    "ScalingIntelligence/qwen3.6-trace-semantic-logic-precision-v1",
    subfolder="iter_50",
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.6-27B")
```

### Merged model (no PEFT at inference)

```python
m = PeftModel.from_pretrained(base, REPO, subfolder="iter_50")
merged = m.merge_and_unload()
merged.save_pretrained("./qwen3.6-trace-slp-iter50-merged")
```

## Evaluation

SWE-bench Verified Pass@1, mini-swe-agent + Modal sandboxes, vLLM @ 128k:

| Model | Pass@1 | Notes |
|---|---:|---|
| Qwen3.6-27B baseline | **58.2%** | phase 1, untrained |
| + `iter_50` LoRA | pending | phase 6 in progress |
| + `iter_100` LoRA | pending | will run after iter_50 finishes |

## Caveats

- Training data is **2 single-turn scenarios** — a narrow slice of the target
  capability. Transfer to SWE-bench's diverse multi-file, multi-turn bugs
  is the open research question this artifact tests.
- Without a KL penalty (`--kl-coef 0.0` in training), late checkpoints
  (`iter_100`, `iter_130`) drift further from the base policy than typically
  desirable. `iter_30` / `iter_50` may transfer better in practice.
- Mutations are stronger than the base scenarios (forced search-misdirection
  via renamed identifiers, decoys, and a contradictory comment); however
  even the mutated scenarios are far simpler than real open-source bugs.

## Citation / acknowledgements

Produced as part of TRACE pipeline development at Stanford
ScalingIntelligence Lab.