Model Description

This model is trained with online reinforcement learning using the RLOO algorithm. Given a target number and a set of allowed numbers, the model produces chain-of-thought reasoning inside <think> tags and a final answer inside <answer> tags. A rule-based verifier rewards correct arithmetic equations (score 1.0), correctly formatted but incorrect equations (score 0.1), and malformed outputs (score 0.0).

Training Details

Evaluation

Evaluated on asingh15/countdown_tasks_3to4 test split (50 prompts) using vLLM with temperature 0.6, top-k 20, top-p 0.95, sampling K=16 responses per prompt.

Usage

python
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from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ba144220/cs224r-default-project-rloo")
tokenizer = AutoTokenizer.from_pretrained("ba144220/cs224r-default-project-rloo")

messages = [{"role": "user", "content": "Using the numbers [3, 4, 6, 8], create an equation that equals 24."}]
input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.6, top_k=20, top_p=0.95, do_sample=True)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Limitations

• Trained and evaluated only on the Countdown arithmetic task; not intended for general-purpose use.
• Performance degrades on harder problems with more numbers or larger targets.
• The 0.5B parameter size limits reasoning capacity compared to larger models.

Authors

Yuchi Hsu (yuchihsu@stanford.edu) and Ryan He (ryanhe@stanford.edu), Stanford CS224R Spring 2026.