Instructions to use Aletheia-Bench/GRPO-Instruct-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Aletheia-Bench/GRPO-Instruct-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Aletheia-Bench/GRPO-Instruct-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Aletheia-Bench/GRPO-Instruct-7B")
model = AutoModelForCausalLM.from_pretrained("Aletheia-Bench/GRPO-Instruct-7B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Aletheia-Bench/GRPO-Instruct-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Aletheia-Bench/GRPO-Instruct-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aletheia-Bench/GRPO-Instruct-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Aletheia-Bench/GRPO-Instruct-7B

SGLang

How to use Aletheia-Bench/GRPO-Instruct-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Aletheia-Bench/GRPO-Instruct-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aletheia-Bench/GRPO-Instruct-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Aletheia-Bench/GRPO-Instruct-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aletheia-Bench/GRPO-Instruct-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Aletheia-Bench/GRPO-Instruct-7B with Docker Model Runner:
```
docker model run hf.co/Aletheia-Bench/GRPO-Instruct-7B
```

wetsoledrysoul commited on 9 days ago

Commit

dc1c7a1

verified ·

1 Parent(s): 3f0cda2

Update model/dataset card: arXiv link, updated abstract, unified documentation

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@@ -1,199 +1,84 @@
 ---
 library_name: transformers
-tags: []
 ---
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-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-#### Preprocessing [optional]
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 ---
 library_name: transformers
+license: cc-by-nc-sa-4.0
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+datasets:
+- Aletheia-Bench/Aletheia-Train
+tags:
+- code
+- code-verification
+- rlvr
+- grpo
 ---
+<font size=3><div align='center'>
+[[**📖 Paper**](https://arxiv.org/pdf/2601.12186)]
+[[**💻 Code**](https://github.com/insait-institute/aletheia)]
+[[**🤗 Models & Datasets**](https://huggingface.co/Aletheia-Bench)]
+</div></font>
+# Aletheia: What Makes RLVR For Code Verifiers Tick?
+Multi-domain thinking verifiers trained via Reinforcement Learning with Verifiable Rewards (RLVR) are a cornerstone of modern post-training. However, their adoption in code generation has lagged behind that of execution feedback due to the prohibitive costs of the full RLVR pipeline. In this work, we ablate three primary choices along the performance-cost trade-off in RLVR: intermediate thinking traces, learning from negative samples, and on-policy training. We introduce **Aletheia**, a controlled, execution-grounded testbed to facilitate a contamination-free analysis of code verifier training recipes across disparate model sizes and covariate shifts across two common verifier application scenarios. Our analysis reveals that the optimal training recipe is scale-dependent: on-policy learning is the primary performance driver for small verifiers, whereas the thinking budget becomes the most vital factor at larger scales. While leveraging negative samples has a consistent impact on top-1 selection accuracy across sizes, their contribution to ranking reconstruction increases monotonically with scale and plays a key role in stabilizing training at large sizes. Our Pareto optimality analysis demonstrates that eliminating on-policy training at larger model scales yields a verifier that performs comparably to the full RLVR recipe. Furthermore, we find that eschewing thinking traces serves as a compute-efficient strategy at lower budgets, offering a strong trade-off between training cost and verifier accuracy. Ultimately, our work provides the empirical foundation necessary to efficiently deploy robust code verifiers, thereby enabling their wider adoption in post-training pipelines for large code generation models.
+## 🤖 This Model
+**`GRPO-Instruct-7B`** is a **GRPO-Instruct** verifier: trained with RLVR (on-policy, with negative samples) but **without intermediate thinking traces**, directly emitting a verdict. It is fine-tuned from [`Qwen/Qwen2.5-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) (7B scale) on [`Aletheia-Bench/Aletheia-Train`](https://huggingface.co/datasets/Aletheia-Bench/Aletheia-Train).
+Given a competitive-programming problem and a set of candidate solutions, the verifier judges and ranks the candidates. See the [GitHub repository](https://github.com/insait-institute/aletheia) for the exact prompt format, plus training and evaluation scripts.
+## 📦 Model Zoo
+Fine-tuned code verifiers at 1.5B, 7B, and 14B scales using several algorithms:
+| Algorithm | Thinking | Negatives | Online | Description |
+| :--- | :---: | :---: | :---: | :--- |
+| [**GRPO-Think**](https://huggingface.co/Aletheia-Bench/GRPO-Think-7B-16k) | ✅ | ✅ | ✅ | Standard GRPO-style approach to training verifiers. |
+| [**GRPO-Instruct**](https://huggingface.co/Aletheia-Bench/GRPO-Instruct-7B) | ❌ | ✅ | ✅ | RLVR training without intermediate thinking traces. |
+| [**RAFT**](https://huggingface.co/Aletheia-Bench/RAFT-7B) | ✅ | ❌ | ✅ | On-policy rejection sampling fine-tuning using only positive reasoning samples. |
+| [**DPO-Think**](https://huggingface.co/Aletheia-Bench/DPO-Think-7B) | ✅ | ✅ | ❌ | Offline preference optimization using pre-collected thinking traces. |
+| [**BatchOnline-GRPO**](https://huggingface.co/Aletheia-Bench/BatchOnline-GRPO-7B) | ✅ | ✅ | ⚠️ | Semi-online training where the generator policy is synced every 4 steps. |
+The `-4k` / `-8k` / `-16k` suffix on GRPO-Think checkpoints denotes the reasoning-token budget (maximum completion length) used during training.
+## 🎁 Datasets
+The Aletheia dataset collection includes:
+* [**Aletheia-Train**](https://huggingface.co/datasets/Aletheia-Bench/Aletheia-Train): 50,000 training instances, each pairing a competitive-programming problem with 2–5 candidate solutions — exactly one of which is correct (execution-verified) — generated by a pool of weak and strong policy models across Python, C++, and Java.
+* [**Aletheia-Train-Questions**](https://huggingface.co/datasets/Aletheia-Bench/Aletheia-Train-Questions): The 3,574 unique problem statements underlying Aletheia-Train, useful for on-policy sampling.
+* [**Aletheia-DPO**](https://huggingface.co/datasets/Aletheia-Bench/Aletheia-DPO): A companion dataset to Aletheia-Train containing "chosen" and "rejected" verification responses for each instance. The chosen response identifies the correct code snippet, while the rejected response does not.
+* [**Aletheia-Mixed**](https://huggingface.co/datasets/Aletheia-Bench/Aletheia-Mixed): A variant of Aletheia-Train in which ~25% of instances carry adversarial modifications that exploit common LLM biases (authority references, self-declared correctness, misleading comments, etc.).
+* [**Aletheia-Mixed-DPO**](https://huggingface.co/datasets/Aletheia-Bench/Aletheia-Mixed-DPO): The DPO-style companion to Aletheia-Mixed.
+* [**Aletheia-Heldout**](https://huggingface.co/datasets/Aletheia-Bench/Aletheia-Heldout): A completely in-distribution test set.
+* [**Aletheia-Strong**](https://huggingface.co/datasets/Aletheia-Bench/Aletheia-Strong): An OOD test set where the candidates are generated by stronger models.
+* [**Aletheia-Hard**](https://huggingface.co/datasets/Aletheia-Bench/Aletheia-Hard): An OOD test set where the comparison between candidates is more difficult.
+* [**Aletheia-Adv**](https://huggingface.co/datasets/Aletheia-Bench/Aletheia-Adv): An OOD test set where the candidates are adversarially modified to exploit common LLM biases.
+## 💡 Intended Uses
+* **RLHF / RLAIF**: plug-and-play reward function for code generation policy optimization.
+* **Automated evaluation**: LLM-as-a-judge for a variety of code-related tasks.
+* **Research**: study the effects of thinking traces, on-policy learning, and negative samples in training successful code verifiers.
+## 📚 Citation
+If you find this work useful, please cite our paper:
+```bibtex
+@misc{venkatkrishna2026aletheiamakesrlvrcode,
+      title={Aletheia: What Makes RLVR For Code Verifiers Tick?},
+      author={Vatsal Venkatkrishna and Indraneil Paul and Iryna Gurevych},
+      year={2026},
+      eprint={2601.12186},
+      archivePrefix={arXiv},
+      primaryClass={cs.SE},
+      url={https://arxiv.org/abs/2601.12186},
+}
+```
+## 📄 License
+This work is licensed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).