49 lines
1.8 KiB
Markdown
49 lines
1.8 KiB
Markdown
---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- grpo
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- rlvr
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- reinforcement-learning
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- countdown
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- reasoning
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- reward-shaping
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---
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# Countdown-Qwen2.5-0.5B-GRPO (dense reward)
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`Qwen2.5-0.5B-Instruct` fine-tuned with **from-scratch GRPO** on the **Countdown** number-puzzle
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task (combine the given numbers exactly once with `+ - * /` to hit a target). Rewards are
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**verifiable** (exact rational arithmetic, whitelisted-AST evaluator) — RLVR: no reward model,
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no critic.
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This is the **best checkpoint**: GRPO at lr `3e-6`, group `8`, `1500` steps, trained with a
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**dense closeness-shaped reward** — for a right-numbers/wrong-value attempt the reward scales
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with proximity to the target (`0.10 + 0.85·exp(-|value-target|/10)`) instead of a flat `0.10`.
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That gives GRPO a gradient on near-misses and broke the ~12% plateau of the step-function reward.
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## Results (dev_public, 300 puzzles, greedy, exact verifier)
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| model | accuracy | easy | medium | hard | avg_tokens (correct) |
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|---|---|---|---|---|---|
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| base Qwen2.5-0.5B-Instruct (floor) | 0.33% | — | — | 0.00% | 20.0 |
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| GRPO, original step reward | 12.00% | 25.83% | 3.33% | 1.67% | 16.9 |
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| **this model (GRPO + dense reward)** | **14.67%** | 29.17% | 7.50% | 0.00% | 17.1 |
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**44× over the base floor** (1/300 → 44/300). Gain concentrated in easy/medium puzzles where the
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model lands near the target. Known failure mode: reasoning collapse — the reward credits only the
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`<answer>`, so `format_rate` stays 0 (no `<think>`).
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo = "s1lv3rj1nx/countdown-qwen2.5-0.5b-grpo-dense"
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model = AutoModelForCausalLM.from_pretrained(repo)
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tok = AutoTokenizer.from_pretrained(repo)
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```
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Trained for the RLVR Arena capstone (RL in Production Bootcamp).
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