Self-bootstrapping recipes for open base LLMs — no human-written training data.
A 14B open base model reaches 80% on HumanEval and 74.4% on HumanEval+ with only a Python interpreter as oracle and no human-curated training data, for under $5 of consumer-GPU compute. This repo contains the recipes, mined pairs, evaluation scripts, and adapters from the paper.
📄 Paper: How Far Can an Open Base Model Self-Improve? Recipes, Limits, and Test-Time Synergy — arXiv link forthcoming
📦 Companion to: ranausmanai/tinyforge (earlier exploratory experiments)
| Model | Setting | Base | After recipe | Δ |
|---|---|---|---|---|
| Qwen2.5-14B-Base | HumanEval (chat-template) | 44/164 (26.8%) | 131/164 (79.9%) | +53.0pp |
| Qwen2.5-14B-Base | HumanEval+ | — | 122/164 (74.4%) | — |
| Qwen2.5-7B-Base | HumanEval (best seed) | 25/164 (15.2%) | 112/164 (68.3%) | +53.0pp |
| Qwen2.5-3B-Base | GSM8K (auto-difficulty curriculum) | 32/100 | 66/100 | +34pp |
| Random external pairs | HumanEval (control) | 25 | 25 | +0 |
All numbers from result.json files in this repo's accompanying paper data. Same adapter under the multi-pair run's eval format reads 132/164 (80.5%) — both round to 80%.
A control experiment — replacing the mined pairs with identically-formatted but randomly-corrupted external pairs — yields exactly +0. The signal is in the self-mined content, not the training-data format.
tinyforge-zero/
├── recipe/ # Training pipelines
│ ├── train_on_pairs.py # Fast-path: train LoRA on a released pairs.jsonl
│ ├── bootstrap.py # Self-bootstrap pipeline (mining + train, 7B / 3B)
│ ├── bootstrap_14b_4bit_harvest.py # 4-bit harvest variant (when full-precision OOMs)
│ ├── multi_pair_14b.py # Aggressive multi-pair variant → 80.5% on 14B
│ ├── curriculum_math.py # Auto-difficulty curriculum for GSM8K (§2.3, §3.8)
│ ├── curriculum_code.py # Auto-difficulty curriculum for code
│ └── math_bootstrap.py # Vanilla math bootstrap (regressed; see §3.8)
├── evals/ # Evaluation harnesses
│ ├── eval_raw.py # HumanEval / MBPP / GSM8K (vLLM, raw-completion)
│ ├── eval_plus.py # HumanEval+ contamination-resistant eval
│ └── confirm.py # Confirmation re-eval against base
├── tts/ # Test-time sampling (§2.2, §3.3)
│ ├── tts_scaling.py # Pass@N scaling sweep (HE, HE+, MATH-500)
│ ├── tts_humaneval.py # Best-of-N pass@1 on HE/HE+
│ ├── tts_math500.py # Best-of-N pass@1 on MATH-500
│ ├── tts_aime.py # Pass@k curve on AIME (k=1..64)
│ ├── tts_qwen14b_recipe.py # TTS on top of the 14B multi-pair adapter
│ └── tts_qwen3_8b_raw_control.py # Control: TTS on raw Qwen3-8B (recipe vs sampling)
├── experiments/ # Every paper experiment, one script each
│ ├── self_consistency.py # §3.4 — deployable TTS via majority vote (no oracle)
│ ├── recipe_x_tts_synergy.py # §3.5 — recipe × TTS synergy threshold (novel finding)
│ ├── cross_domain_code_to_math.py # §3.10 — code-trained recipe on math (+2, marginal)
│ ├── mbpp_seeded_cross_arch.py # §3.9 — Llama/Coder cross-architecture self-mining
│ ├── diversity_cued_mining.py # §3.10 — diversity-cued mining (low yield)
│ ├── recursive_bootstrap.py # §3.10 — recursive iter1→iter2→iter3 (plateau)
│ ├── self_correction_code.py # §3.10 — code self-correction recipe
│ ├── self_correction_math_naive.py # §3.10 — naive (wrong→fix only): catastrophic regress
│ ├── self_correction_math_fixed.py # §3.10 — fixed (mixed positives): recovered
│ ├── math500_seeded_mining.py # §3.10 — distribution-mismatch demo (catastrophic)
│ ├── aime_scaling.py # AIME pass@k = 1..64 sweep
│ ├── bcb_hard_eval.py # §3.10 — BigCodeBench-Hard distribution mismatch
│ └── star_baseline_gsm8k.py # Related-work baseline (STaR / rejection sampling FT)
├── controls/
│ └── mbpp_corrupt_control.py # §3.6 — the +0 negative-control experiment
├── data/ # Released mined pairs (drove paper numbers)
│ ├── pairs_7b_40.jsonl # 40 pairs for Qwen2.5-7B-Base
│ ├── pairs_14b_multi_new60.jsonl # 60 aggressive-mined pairs for 14B (+ warmup 40 = 100)
│ └── pairs_math_13.jsonl # 13 curriculum-mined math pairs (3B GSM8K)
├── docs/
│ ├── recipe_diagram.png # The 5-stage recipe diagram (rendered above)
│ ├── scaling_chart.png # Recipe lift vs base capability (paper Fig 1)
│ ├── fig1_headline.png # Headline result chart
│ └── fig6_boundary.png # Boundary conditions across 9 models
├── scripts/
│ └── make_recipe_diagram.py # Source for the rendered recipe diagram
├── REPRODUCE.md # Paper claim → exact command mapping (all sections)
├── requirements.txt
└── LICENSE
A note on these scripts: recipe/, evals/, and controls/ are the clean replication paths — these have argparse CLIs and produce the headline numbers. The scripts under experiments/ and tts/ are the original research scripts used to produce each figure / table in the paper. They work, but they're closer to "research code" than "production tooling" — argument names vary, some have hard-coded paths to /workspace/, and they were each run on RunPod with a specific GPU. Read the top-of-file docstring of any experiment script for what it does and how to invoke it.
# 1. Clone
git clone https://github.com/ranausmanai/tinyforge-zero.git
cd tinyforge-zero
# 2. Install (Python 3.10+, CUDA 12.1+, GPU with ≥40GB VRAM recommended)
pip install -r requirements.txt
# 3. Baseline the model (so you know the lift is real)
python evals/eval_raw.py \
--model Qwen/Qwen2.5-7B \
--bench humaneval
# 4. Train on the released 40 mined pairs (~10 min on H100)
python recipe/train_on_pairs.py \
--model Qwen/Qwen2.5-7B \
--pairs data/pairs_7b_40.jsonl \
--epochs 2 --lr 1e-4 --lora-rank 16 \
--out adapter_7b --seed 13
# 5. Evaluate the trained adapter
python evals/eval_raw.py \
--model Qwen/Qwen2.5-7B \
--adapter adapter_7b \
--bench humanevalExpected outcome: HumanEval moves from ~25/164 to ~95–112/164 (seed-dependent).
For the 14B → 80.5% run, use recipe/multi_pair_14b.py with both data/pairs_7b_40.jsonl (warmup) and data/pairs_14b_multi_new60.jsonl. See REPRODUCE.md for the exact command and expected hardware.
The recipe works under stated conditions. We document four failure modes:
- Saturation: Qwen3-8B/14B-Base and Qwen2.5-72B-Base have so little headroom on HumanEval that mining produces zero or negative lift.
- Distribution mismatch: Pairs mined on simple problems do not transfer to BigCodeBench-Hard (library code) or MATH-500 (competition math). Catastrophic when ignored — see the over-correction case (Qwen3-4B MATH-500 dropped 299 → 69).
- Base capability floor: OLMo-2-7B at 5/164 baseline produces too few "fix" attempts to mine from.
- Self-correction trained on wrong→fix only: model over-doubts and degrades on correct outputs. Mixing right→stays-right traces recovers it.
See the paper's §3 for measurements; the boundary chart above shows the recipe's lift across all 9 base models we tested.
The LoRA adapter weights for the headline 14B run (the 80.5% adapter) are ~200 MB and are not committed to this repo. They live separately:
- Hugging Face Hub:
ranausmans/tinyforge-zero-qwen25-14b-lora— 192 MB, Apache-2.0 (inherits from Qwen2.5-14B base)
The adapter is a standard peft LoRA over Qwen/Qwen2.5-14B. Load with:
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-14B", torch_dtype="bfloat16")
model = PeftModel.from_pretrained(base, "ranausmans/tinyforge-zero-qwen25-14b-lora")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-14B")| Run | GPU | Time | Cost |
|---|---|---|---|
| Qwen2.5-7B 40-pair recipe | RTX 6000 Ada | ~30 min | <$1 |
| Qwen2.5-14B multi-pair (80.5%) | 1× H100 80GB | ~95 min | ~$3.50 |
| Qwen2.5-3B GSM8K curriculum | RTX 6000 Ada | ~30 min | <$1 |
| Full eval suite (9 models, HE+HE++MBPP) | 1× H100 | ~3 hrs | ~$8 |
All runs were on rented consumer/cloud GPUs (RunPod). Total spend documented in the paper was under $50.
@misc{usman2026tinyforgezero,
title = {How Far Can an Open Base Model Self-Improve?
Recipes, Limits, and Test-Time Synergy},
author = {Rana Usman},
year = {2026},
eprint = {TBD},
archivePrefix = {arXiv},
primaryClass = {cs.AI}
}MIT — see LICENSE. The mined pairs in data/ are derivatives of base-model outputs (Qwen2.5 family, Apache-2.0). Treat downstream redistribution accordingly.
- Issues / questions: GitHub Issues
- Email: usmanashrafrana@gmail.com