| --- |
| license: apache-2.0 |
| base_model: google/gemma-4-E4B-it |
| tags: |
| - gemma4 |
| - code |
| - agent |
| - grpo |
| - reinforcement-learning |
| - swe-bench |
| - tree-of-reasoning |
| pipeline_tag: text-generation |
| language: |
| - en |
| library_name: transformers |
| --- |
| |
| # 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. |
| |
| --- |
| |
| ## 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. |
|
|
| --- |
|
|
| ## Quickstart |
|
|
| ```python |
| 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)) |
| ``` |
|
|
| --- |
|
|
| ## 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 |
|
|
