Below is a suggested README.md for your GitHub repository. It outlines the project’s aim, background, architecture, roadmap, datasets, and basic usage. Feel free to adapt it to your specific needs.
# Verifier → Plan → Apply (VPA)
VPA is a lightweight framework for improving the precision and reliability of large language models (LLMs) without resorting to unstable reinforcement learning. It wraps a base LLM with a **Verifier → Plan → Apply** loop: the model generates multiple candidate answers, a verifier ensemble scores and diagnoses them, a planner proposes simple self-edits, and small LoRA adapters are trained on verified corrections. Only updates that pass a regression gate are merged back into the system, preventing catastrophic forgetting.
## ✨ Key Motivation
Current LLMs are powerful but prone to hallucinations, incorrect code/math outputs, and inconsistent formatting. Existing approaches to self-improvement, such as iterative self-refinement or RL fine-tuning, either require expensive reinforcement learning or risk destabilizing the model. VPA aims to provide a **stable, sample-efficient alternative** that:
- Improves exact-match accuracy, pass@k on coding tasks, and reduces hallucinations.
- Calibrates model confidence via abstention mechanisms.
- Persists fixes across sessions by storing them in small adapter modules.
- Avoids the instability and high cost of “inner-loop” RL.
## 🧠 What VPA Does
1. **Draft**: The base model (e.g., a 7B–13B transformer) generates *k* candidate answers for a query, optionally using tools (retrieval, Python execution, calculator).
2. **Verify**: An ensemble of verifiers scores each candidate on factuality (citation overlap, contradiction detection), formal correctness (unit tests, schema validators), and style/safety.
3. **Plan**: A small planner proposes a self-edit plan (e.g., adjust *k*, pick tools, choose LoRA learning rates/epochs) based on verifier diagnostics.
4. **Apply**: Verified corrections are turned into training pairs, and a LoRA adapter is fine-tuned via supervised learning (SFT/DPO). Only tiny ranks are used to keep updates efficient.
5. **Gate**: A frozen regression suite (stability set) tests each adapter. Only those that improve performance without regressions (e.g., ≥ 3 EM points on QA, ≥ 5 pass@1 points on MBPP) are accepted.
6. **Route**: Successful adapters are merged or routed based on domain similarity (law, math, code). Failed adapters are quarantined and not merged.
## 📊 Research Questions
- **RQ1:** Does VPA improve precision (exact match, F1, pass@k, hallucination ↓) versus baselines like best-of-N and Self-Refine?
- **RQ2:** Can we persist fixes (no context) without forgetting?
- **RQ3:** What’s the cost–performance frontier compared with inner-loop RL?
- **RQ4:** Which verifier signals matter most (ablation)?
## 📦 Datasets & Metrics
- **Factual QA:** FEVER, HotpotQA, NQ-Open (Exact Match/F1, citation precision/recall, hallucination rate).
- **Coding:** HumanEval(+), MBPP (pass@k).
- **Math/Reasoning:** GSM8K, a MATH subset (accuracy, step validity).
- **Calibration:** Brier score, Expected Calibration Error (ECE) with abstention rates.
- **Stability:** Performance delta on a “no-regression” holdout after each adapter merge.
## 🚀 Roadmap
### MVP (Weeks 1–2)
- **Week 1**
- Scaffold the repository and set up a basic evaluation harness.
- Implement *k*-draft generation with tool usage.
- Build a preliminary verifier ensemble (factual, formal, style).
- Run baseline experiments (base model + best-of-N) on small data slices.
- **Week 2**
- Implement a simple planner using rejection sampling over a small action grid.
- Create the Apply module: LoRA SFT on verified corrections.
- Add the regression Gate and show one “sticky” improvement that persists.
### Full System (Weeks 3–8)
- **Week 3:** Wire up full datasets; add advanced verifiers (citation checker, contradiction model); start drafting paper figures.
- **Week 4:** Implement adapter routing and anti-forgetting (e.g., KL regularization, replay).
- **Week 5:** Perform ablations (remove verifier channels, random planner, no gate).
- **Week 6:** Optional RL ablation to contrast with VPA; finalize results.
- **Week 7:** Write paper, generate plots (e.g., Pareto curves), and prepare reproducibility materials (configs, seeds).
- **Week 8:** Polish code, finalize paper, and submit (arXiv + workshop or conference).
## 🛠️ Repository Structure
vpa/ README.md # This file configs/ base.yaml # Base model and LoRA hyper-parameters planner_grid.yaml # Planner action grid vpa/ draft.py # k-sampling + tool API wrapper verify/ factual.py # Citation/overlap/contradiction checks formal.py # Unit tests, schema validators score.py # Composite scoring & calibration plan/ planner.py # Rejection-sampling planner apply/ sft.py # LoRA SFT/DPO training gate.py # Regression harness route.py # Adapter router data/ builders.py # Construct (x, y*, evidence) from raw inputs eval/ qa_eval.py # QA evaluation scripts code_eval.py # Coding evaluation math_eval.py # Math evaluation scripts/ run_mvp.sh # Script to run MVP pipeline train_adapter.sh # Script to train LoRA adapters
## 🤝 Contributing
Contributions are welcome! If you’d like to add new verifiers, expand the planner search space, or test on new datasets, please open an issue or submit a pull request. We adhere to a code of conduct that expects respectful and constructive collaboration.
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*Disclaimer: This project is for research purposes. Please ensure you have the right to use any datasets you contribute and respect all applicable licenses.*
This README provides a high-level overview and a clear roadmap. You can adjust section names, add installation instructions (e.g., how to download models or datasets) and update the roadmap as the project evolves.