wheattoast11/OmniCoder-9B-Zero-Phase2

OmniCoder-9B-Zero-Phase2

CARL Phase 1' — VLM grounding checkpoint. EVAL: PASS (94.6% click accuracy).

A LoRA adapter trained with vision GRPO for GUI grounding. The model understands screenshots and produces structured coordinate output for click targets.

Results

Training

• —Method: Vision GRPO with CARL cascade rewards
• —Base model: Tesslate/OmniCoder-9B (Qwen3.5-9B VLM)
• —SFT substrate: wheattoast11/OmniCoder-9B-Zero-Phase2-Vision-SFT
• —Steps: 500 GRPO steps
• —Hardware: 1x L40S 48GB, bf16, LoRA r=64
• —Dataset: wheattoast11/grounding-with-images (20K samples)

Phase Transition Observed

During SFT, the model exhibited a first-order phase transition:

• —Steps 0-10: Baseline (3% accuracy, entropy 1.0)
• —Steps 10-20: Melting (entropy spikes to 9.3)
• —Steps 20-25: Transition (accuracy jumps 57 points in 5 steps)
• —Steps 25-35: Crystallization (99% accuracy, entropy 0.4)
• —Steps 35-46: Converged (99.3%, entropy 0.12)

Consistent with Kuramoto synchronization in coupled oscillator systems.

Theoretical Foundation

1. 1.Bounded Informational Time Crystals — DOI: 10.5281/zenodo.18906944
2. 2.Material Reality — DOI: 10.5281/zenodo.18992029
3. 3.Semantic Realizability — DOI: 10.5281/zenodo.18992031

Usage

from transformers import AutoModelForImageTextToText, AutoProcessor
from peft import PeftModel

base = AutoModelForImageTextToText.from_pretrained(
    "Tesslate/OmniCoder-9B",
    torch_dtype="bfloat16",
    device_map="cuda:0",
)
model = PeftModel.from_pretrained(base, "wheattoast11/OmniCoder-9B-Zero-Phase2")
model = model.merge_and_unload()

processor = AutoProcessor.from_pretrained(
    "Tesslate/OmniCoder-9B",
    min_pixels=256*28*28,
    max_pixels=1280*28*28,
)

Citation

@article{desai2026carl,
  title   = {Coherence-Aware Reinforcement Learning},
  author  = {Desai, Tej},
  year    = {2026},
  url     = {https://github.com/wheattoast11/carl},
  note    = {Intuition Labs LLC}
}

License

Apache 2.0 — Intuition Labs LLC