Peta1: Gemma 4 E4B Tree-of-Reasoning & Verifiable Outcome GRPO

Peta1 is an autonomous software engineering agent checkpoint based on google/gemma-4-E4B-it (4.3B active parameters), post-trained using a targeted three-stage curriculum:

1. Tree-of-Reasoning (ToR) SFT: Supervised deliberation in Gemma 4's native latent thinking tokens (<|channel|>thought) enforcing hypothesis formulation, environment simulation, and branch pruning.
2. Capability Frontier Discovery: Screening of candidate tasks (G = 4, T = 0.7) to isolate the 18% informative frontier band (0 < p < 1.0), preventing policy gradient collapse from trivial (36%) and impossible (46%) tasks.
3. Verifiable Outcome GRPO: Policy gradient optimization with verifiable rewards (R_target + R_format + R_tor - lambda * length) and Group Relative Advantage estimation driven entirely on Dual Tesla T4 GPUs.

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Benchmark Results

1. Full Official SWE-bench Lite (n = 300)

Evaluated on the full official test split of princeton-nlp/SWE-bench_Lite:

Checkpoint	Solved / Total	Resolution Rate	95% Wilson CI
Base Gemma 4 E4B	25 / 300	8.33%	[5.71%, 12.01%]
Peta1 (Stage 3 ToR GRPO)	101 / 300	33.67%	[28.56%, 39.19%]
Absolute Gain (Delta)	+76 tasks	+25.33pp	p = 5.23e-22

Paired Contingency Table (n = 300):

• Mutual Failure: 198
• Mutual Success: 24
• Solved ONLY by Peta1: 77 tasks
• Regressions (Solved ONLY by Base): 1 task
• Two-tailed exact McNemar test: chi^2 = 72.12, p = 5.23 × 10^-22.

2. The Navigation vs. Comprehension Dissociation

In a controlled 2-turn agentic evaluation across 20 repository tasks:

• Turn 1 (Autonomous Tool Navigation): Base Gemma 4 achieves 0.0% (0/20), while Peta1 achieves 25.0% (5/20) (+25.0pp gain).
• Turn 2 (Grounded Code Comprehension): When grounded file context is provided, both Base and Peta1 achieve 100.0% (20/20) resolution.
• This proves compact models possess strong latent code comprehension; post-training specifically cures exploration failure and tool navigation.

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Quickstart

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "manojpanda/peta1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

messages = [
    {"role": "user", "content": "Locate the bug in flask/wrappers.py where JSON decoding does not respect request encoding."}
]

prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)

print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=False))

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Hardware & Training Details

• Hardware: 2x NVIDIA Tesla T4 (15 GiB VRAM each, Kaggle Free Tier)
• Precision: Full FP16 (zero quantization loss)
• Parameters: 4.3B active parameters
• License: Apache 2.0