---
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: transformers
pipeline_tag: text-generation
tags:
  - grpo
  - rlvr
  - reinforcement-learning
  - countdown
  - reasoning
  - reward-shaping
---

# Countdown-Qwen2.5-0.5B-GRPO (dense reward)

`Qwen2.5-0.5B-Instruct` fine-tuned with **from-scratch GRPO** on the **Countdown** number-puzzle
task (combine the given numbers exactly once with `+ - * /` to hit a target). Rewards are
**verifiable** (exact rational arithmetic, whitelisted-AST evaluator) — RLVR: no reward model,
no critic.

This is the **best checkpoint**: GRPO at lr `3e-6`, group `8`, `1500` steps, trained with a
**dense closeness-shaped reward** — for a right-numbers/wrong-value attempt the reward scales
with proximity to the target (`0.10 + 0.85·exp(-|value-target|/10)`) instead of a flat `0.10`.
That gives GRPO a gradient on near-misses and broke the ~12% plateau of the step-function reward.

## Results (dev_public, 300 puzzles, greedy, exact verifier)

| model | accuracy | easy | medium | hard | avg_tokens (correct) |
|---|---|---|---|---|---|
| base Qwen2.5-0.5B-Instruct (floor) | 0.33% | — | — | 0.00% | 20.0 |
| GRPO, original step reward | 12.00% | 25.83% | 3.33% | 1.67% | 16.9 |
| **this model (GRPO + dense reward)** | **14.67%** | 29.17% | 7.50% | 0.00% | 17.1 |

**44× over the base floor** (1/300 → 44/300). Gain concentrated in easy/medium puzzles where the
model lands near the target. Known failure mode: reasoning collapse — the reward credits only the
`<answer>`, so `format_rate` stays 0 (no `<think>`).

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "s1lv3rj1nx/countdown-qwen2.5-0.5b-grpo-dense"
model = AutoModelForCausalLM.from_pretrained(repo)
tok = AutoTokenizer.from_pretrained(repo)
```

Trained for the RLVR Arena capstone (RL in Production Bootcamp).