🧠 Autonomous Recursive Code-Improving Agent (Experimental) with rsi-gpt 20b

This repository demonstrates an experimental autonomous coding agent that can iteratively improve code, generate new tests (including edge cases), evaluate itself in a sandbox, and self-train via LoRA using its own interaction data.

The system closes a full loop:

write → test → reflect → improve → generate data → fine-tune → repeat

It is designed as a research prototype for studying recursive self-improvement, agentic programming, and self-generated supervision.

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🔍 What This Project Does

At a high level, the agent:

1. Starts with a small codebase and basic tests
2. Runs tests in an isolated sandbox
3. Uses an LLM to propose code patches
4. Generates new edge-case tests for its own changes
5. Re-runs tests to measure improvement
6. Logs successful interactions as supervised fine-tuning (SFT) data
7. Periodically fine-tunes itself using LoRA
8. Repeats until performance plateaus

The goal is not perfection, but measurable improvement over time.

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🧩 Core Components

Workspace

A local sandbox directory containing:

• Initial example code (example.py)
• Initial tests (test_example.py)
• All patches and generated tests

This workspace is mutated over time by the agent.

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Sandbox

Tests are executed via pytest inside a controlled subprocess with:

• Timeout protection
• Captured output
• Binary success/failure signal

This prevents runaway execution while giving the agent concrete feedback.

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Agent State

The agent maintains memory across iterations:

• Success history
• Reward signals
• Files modified
• Plateau counter (to detect stagnation)

This state governs when the system should stop.

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Autonomous Agent Loop

Each iteration performs:

• Patch phase
  The model proposes code improvements based on test output.

• Test-generation phase
  The model generates new tests, focusing on edge cases.

• Evaluation phase
  Tests are re-run to check whether things improved.

• Archival phase
  Successful prompt–response pairs are saved as .jsonl samples for training.

The agent communicates strictly via structured JSON to reduce ambiguity.

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Recursive Depth

Each decision (patch or test generation) allows multiple retries if output is malformed or incomplete. This increases robustness without human intervention.

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🧪 Self-Training via LoRA

All successful interactions are converted into supervised fine-tuning samples.

Training uses:

• LoRA adapters
• 4-bit quantized base model
• Short training bursts every iteration
• Conservative hyperparameters to avoid catastrophic drift

This allows the agent to learn from its own successes over time.

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📈 Improvement & Plateau Detection

Improvement is estimated by:

• Increase in passing test count
• Comparison against prior iterations

If no improvement is detected for several iterations, training stops automatically.

This prevents infinite loops and overfitting to noise.

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🛠 Model & Stack

• Base model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
• Training: TRL SFTTrainer
• Optimization: AdamW 8-bit
• Frameworks: PyTorch, Hugging Face Datasets, Unsloth
• Testing: Pytest

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⚠️ Important Notes

• This is a research prototype, not a production system.
• Improvement signals are approximate, not formal verification.
• Generated code and tests should not be trusted blindly.
• Recursive self-training can drift if reward signals are poorly defined.

Treat this as a laboratory, not an autopilot.

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🌱 Why This Matters

This project explores a critical question:

Can models improve their practical capabilities by generating, evaluating, and learning from their own work—without external labels?

The answer is still “partially, cautiously, and experimentally,” but this repository provides a concrete, inspectable loop for studying that question.

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📜 License & Use

Use responsibly. Expect bugs. Expect surprises.
Recursive systems amplify both intelligence and mistakes.

Curiosity recommended. Blind faith discouraged.

Uploaded finetuned model

• Developed by: EpistemeAI
• License: apache-2.0
• Finetuned from model : unsloth/gpt-oss-20b-unsloth-bnb-4bit

This gpt_oss model was trained 2x faster with Unsloth and Huggingface's TRL library.