FIM-14B
📄 Paper · 💻 GitHub · 🤗 Dataset · 🤗 Collection
FIM-14B is the 14B coding-agent model of "Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models": Qwen2.5-Coder-14B-Instruct, mid-trained on function-aware FIM data, then post-trained on R2E-Gym agent trajectories with the upstream recipe unmodified. The mid-training stage is the only difference from a standard R2E-Gym reproduction — worth +3.0 points on SWE-Bench-Verified and +4.0 on SWE-Bench-Lite, while also recovering most of the general-capability erosion that agentic post-training inflicts (LiveCodeBench +11.1, τ-bench +3.9, BFCL +2.4 over the post-training-only arm).
Training pipeline
- Base model:
Qwen/Qwen2.5-Coder-14B-Instruct - FIM mid-training:
midtraining/configs/fim_midtrain.yamlon TIGER-Lab/FIM-Midtraining-400K (as-run copy:FIM_Midtrain_14B.yaml) → intermediate checkpoint released as TIGER-Lab/FIM-Mid-14B - Post-training: SFT on R2E-Gym agent trajectories —
posttraining/r2egym/(as-run copy:FIM_Posttrain_14B.yaml)
Results
Means over three evaluation seeds, identical harness for both arms (paper Tables 1–2):
| Setting | SWE-Bench-Verified | SWE-Bench-Lite |
|---|---|---|
| Qwen2.5-Coder-14B-Instruct + R2E-Gym (reproduced) | 26.20 | 18.00 |
| FIM-14B (+ FIM mid-training) | 29.20 | 22.00 |
| Δ | +3.00 | +4.00 |
Capability preservation at 14B (six benchmarks outside SWE-Bench):
| Setting | LiveCode | OJBench | FSB-EN | Terminal | τ-bench | BFCL | Avg |
|---|---|---|---|---|---|---|---|
| + R2E-Gym only | 24.10 | 2.80 | 47.72 | 2.41 | 3.40 | 15.80 | 16.04 |
| FIM-14B | 35.20 | 4.74 | 48.25 | 3.66 | 7.30 | 18.20 | 19.56 |
Reproduction guides for all six: evaluation/.
Evaluate on SWE-Bench Verified
FIM-14B is evaluated with the R2E-Gym agent scaffold (fixed by its post-training pipeline). The complete pinned walkthrough lives at evaluation/swebench/released_checkpoints.md.
1. Serve the model with vLLM
CUDA_VISIBLE_DEVICES=0 \
VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
python -m vllm.entrypoints.openai.api_server \
--model TIGER-Lab/FIM-14B \
--served-model-name FIM-14B \
--host 127.0.0.1 \
--port 8400 \
--tensor-parallel-size 1 \
--max-model-len 65536 \
--hf-overrides '{"max_position_embeddings": 65536}' \
--enable-prefix-caching \
--gpu-memory-utilization 0.9 \
> vllm_fim14b.log 2>&1 &
Wait until the server is up (model load takes ~1 minute):
curl -s http://127.0.0.1:8400/v1/models
2. Run the agent on SWE-Bench Verified
From an upstream, unmodified R2E-Gym checkout (Docker required):
export OPENAI_API_KEY=EMPTY
export LLM_BASE_URL="http://127.0.0.1:8400/v1"
uv run python src/r2egym/agenthub/run/edit.py runagent_multiple \
--dataset "R2E-Gym/SWE-Bench-Verified" \
--split "test" \
--start_idx 0 \
--k 500 \
--traj_dir "./traj" \
--exp_name "FIM-14B_swebench_verified_r1" \
--llm_name "openai/FIM-14B" \
--scaffold "r2egym" \
--backend "docker" \
--use_fn_calling False \
--temperature 0 \
--max_steps 40 \
--max_steps_absolute 100 \
--max_workers 6 \
--max_reward_calc_time 1200 \
--max_tokens 65536 \
--use_existing True
For SWE-Bench Lite, use --dataset "R2E-Gym/SWE-Bench-Lite" --k 300.
3. Score with the official SWE-bench harness
Convert the trajectories to a submission and score with the official harness — evaluation/swebench/score.sh. The reported number is resolved_instances / total_instances.
Citation
@article{wang2026fim,
title={Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models},
author={Wang, Yubo and Liang, Jiarong and Zhang, Yuxuan and Liu, Xuye and Wei, Cong and Zhang, Yuyu and Nie, Ping and Chen, Wenhu},
journal={arXiv preprint arXiv:2607.12463},
year={2026}
}