---
title: Logics-PPT-Qwen3.6-27B-RL
canonical_url: "https://www.modelscope.ai/models/Alibaba-DT/Logics-PPT-Qwen3.6-27B-RL"
md_url: "https://www.modelscope.ai/models/Alibaba-DT/Logics-PPT-Qwen3.6-27B-RL.md"
repository: Alibaba-DT/Logics-PPT-Qwen3.6-27B-RL
last_updated: 2026-09-21
license: apache-2.0
model_type:
  - qwen3_5
architectures:
  - Qwen3_5ForConditionalGeneration
base_model:
  - Logics-MLLM/Logics-PPT-Qwen3.6-27B-SFT
base_model_relation: finetune
parameters: 27.4B
tensor_type:
  - BF16
library_name:
  - pytorch
  - transformer
  - safetensors
frameworks:
  - pytorch
downloads: 171
stars: 0
tags:
  - presentation-generation
  - html-generation
  - slide-generation
  - code-generation
  - reinforcement-learning
  - grpo
  - verifiable-rewards
  - agentic-post-training
  - multimodal-layout
  - openclaw
---

# Logics-PPT-Qwen3.6-27B-RL

> Logics-PPT-Qwen3.6-27B-RL - An open-source model by Alibaba-DT on ModelScope. Logics-PPT-Qwen3.6-27B-RL

Alibaba-DT/Logics-PPT-Qwen3.6-27B-RL is a 27.4B-parameter machine learning model on ModelScope. licensed under apache-2.0. derived from Logics-MLLM/Logics-PPT-Qwen3.6-27B-SFT.

- **Repository**: Alibaba-DT/Logics-PPT-Qwen3.6-27B-RL
- **License**: apache-2.0
- **Parameters**: 27.4B
- **Base model**: Logics-MLLM/Logics-PPT-Qwen3.6-27B-SFT
- **Tags**: presentation-generation, html-generation, slide-generation, code-generation, reinforcement-learning, grpo, verifiable-rewards, agentic-post-training, multimodal-layout, openclaw
- **Downloads**: 171
- **Stars**: 0
- **Last updated**: 2026-09-21

Source: https://www.modelscope.ai/models/Alibaba-DT/Logics-PPT-Qwen3.6-27B-RL

---

# Logics-PPT-Qwen3.6-27B-RL

> Model card for Logics-PPT-Qwen3.6-27B-RL. The technical report is under review and a public link will be added when available.

## 📰 News

- **[2026.09.20]** Released **Logics-PPT-Qwen3.6-27B-RL** under the Apache-2.0 license.
- This model is the reinforcement-learning checkpoint built on top of the SFT presentation-generation model.
- The technical report is currently under review. A citation link will be added after public release.

## 🔎 Overview

**Logics-PPT-Qwen3.6-27B-RL** is a 27B-parameter model for automatic presentation generation.
It is initialized from **Logics-PPT-Qwen3.6-27B-SFT** and further optimized with
**GRPO-based reinforcement learning** using **verifiable aesthetic rewards** computed on
fully rendered HTML slides.

The model targets **single-page HTML presentation generation** in the **OpenClaw** framework.
Instead of relying on an LLM judge for visual refinement, this RL stage uses deterministic,
rule-based rewards defined on post-render outputs.

### RL objective

The RL stage refines slide aesthetics along five verifiable dimensions:

1. **aspect-ratio compliance**
2. **whitespace adequacy**
3. **element-collision avoidance**
4. **visual balance**
5. **font readability**

These rewards are computed on rendered slides using a browser-based rendering pipeline
with headless Chromium / Playwright. The reward design is intended to provide a transparent,
auditable signal for aesthetic refinement beyond text-only supervision.

## 🧠 Training Recipe

### Initialization

The paper selects the RL initialization checkpoint by choosing the SFT model that:
- preserves performance on the GDPVal PPT subset, while
- achieving a high score on PresentBench.

For the 27B dense backbone, the initialization is the selected SFT checkpoint released as:

- **Logics-PPT-Qwen3.6-27B-SFT**

### RL data selection

The paper studies two RL data-selection strategies:

- **task-hard**
- **aesthetic-hard**

These are designed to expose the policy to:
- globally difficult presentation-generation tasks, and
- model-specific visual failure cases.

### RL optimization

The policy is optimized with **GRPO** under long-horizon agentic rollouts.

Reported shared RL settings include:

- 8 prompt instances per step
- group size 8
- 64 rollouts per update
- rollout temperature 1.0
- up to 128 actions per trajectory
- total sequence budget 131,072 tokens
- one training epoch

The paper also studies runs with and without an explicit KL anchor to the SFT initialization.

## 📊 Experimental Results




### Verifiable aesthetic scores

The strongest reported 27B RL variant in the paper reaches:

| Metric group | PresentBench | GDPVal (PPT subset) |
| --- | ---: | ---: |
| Aspect ratio | 1.00 | 0.75 |
| Whitespace | 0.89 | 0.92 |
| Element collision avoidance | 0.76 | 0.60 |
| Visual balance | 0.57 | 0.53 |
| Font readability | 0.98 | 0.82 |
| **Total** | **4.20** | **3.62** |

This demonstrates that RL improves several post-render visual properties relative to the no-train
baseline and often relative to SFT on the rule-based reward dimensions.

## 🎯 Intended Use

This model is intended for **research use** in:

- reinforcement learning for presentation generation
- verifiable post-render reward design
- HTML-based layout generation
- agentic slide generation
- aesthetic refinement of long-horizon LLM outputs

This model is especially suitable for work studying:
- rule-based visual rewards,
- RL refinement after SFT,
- post-render evaluation and optimization.

## ⚠️ Limitations

- This RL model is optimized for **verifiable aesthetic metrics**, not solely for benchmark-level
  semantic quality.
- As reported in the paper, the RL stage primarily improves the five rule-based visual dimensions;
  users interested in maximum overall benchmark score may prefer the SFT checkpoint depending on use case.
- The model remains optimized for **single-page HTML slide generation**.
- Real usage still depends on the surrounding agent scaffold, rendering pipeline, and prompt format.
- The paper’s future-work discussion explicitly notes that content-quality reward modeling remains open.

## 🚀 Quickstart

### Transformers: text-generation smoke test

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Logics-MLLM/Logics-PPT-Qwen3.6-27B-RL"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
)
model.eval()

messages = [{
    "role": "user",
    "content": (
        "Generate a visually clean 5-slide presentation as separate HTML slides. "
        "Keep a 16:9 canvas, avoid overlapping elements, reduce excessive whitespace, "
        "and maintain readable fonts."
    ),
}]

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

with torch.inference_mode():
    output = model.generate(
        **inputs,
        max_new_tokens=2048,
        do_sample=True,
        temperature=1.0,
        top_p=0.95,
        top_k=20,
    )

completion = output[0, inputs["input_ids"].shape[-1]:]
print(tokenizer.decode(completion, skip_special_tokens=True))
```


This is only a minimal text-generation smoke test.  
Actual presentation generation in our setup additionally uses:

- the OpenClaw-style agent framework,
- task instruction files and reference materials,
- browser rendering



## 📚 Citation

The technical report is currently under review.  
Citation information will be added after public release.

## 🙏 Acknowledgements

We thank the Qwen, GLM, OpenClaw, PresentBench, and GDPVal teams and the broader research communities working on agentic generation, presentation intelligence, and reinforcement learning for LLMs.

## License

The model weights are released under the Apache License 2.0.