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Model: s1lv3rj1nx/countdown-qwen2.5-0.5b-grpo-lr3e6
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
+---
+
+# Countdown-Qwen2.5-0.5B-GRPO (lr 3e-6 ablation)
+
+`Qwen2.5-0.5B-Instruct` fine-tuned with **from-scratch GRPO** (Group-Relative Policy
+Optimization) on the **Countdown** number-puzzle task — combine the given numbers exactly
+once with `+ - * /` to hit a target. Rewards are **verifiable** (exact rational arithmetic
+via a whitelisted-AST evaluator), so this is RLVR: no reward model, no critic.
+
+This checkpoint is the **best ablation** (learning rate `3e-6`, group `8`, `1500` steps).
+
+## Results (dev_public, 300 puzzles, greedy, exact verifier)
+
+| model | accuracy | hard (5-num) | format_rate | avg_tokens (correct) |
+|---|---|---|---|---|
+| base Qwen2.5-0.5B-Instruct (floor) | 0.33% | 0.00% | 0.00% | 20.0 |
+| **this model (GRPO, lr 3e-6)** | **12.00%** | **1.67%** | 0.00% | 16.9 |
+
+**36x over the base floor** (1/300 -> 36/300). Ablations: learning rate was the only lever
+that moved accuracy (1e-6 -> 3e-6 gave 1% -> 12%); more steps (3000) and larger group (16)
+both plateaued. Known failure mode: reasoning collapse — the shaped reward scores only the
+`<answer>`, so the model emits bare answers with no `<think>` (format_rate = 0).
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("<your-username>/countdown-qwen2.5-0.5b-grpo-lr3e6")
+tok = AutoTokenizer.from_pretrained("<your-username>/countdown-qwen2.5-0.5b-grpo-lr3e6")
+```
+
+Trained for the RLVR Arena capstone (RL in Production Bootcamp).