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Model: s1lv3rj1nx/countdown-qwen2.5-0.5b-grpo-dense
Source: Original Platform

README.md

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+---
+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).