metadata
license: other
base_model: Qwen/Qwen3-4B-Instruct-2507
tags:
- grpo
- verl
- math-reasoning
- harpm
Hint_Model_GRPO_V1
GRPO-trained Qwen3-4B-Instruct-2507 with HARPM (Hard-problem Adaptive Reference-Prompt Matching) hint injection.
Method
- Phase 1: 1 epoch plain GRPO on quarter training set → identify 8/8-fail hard problems (1709 found).
- Phase 2: annotate 6-dim features with local Qwen → nearest-neighbor match against a hard reference set → inject reference problem+solution into prompts (597 problems hinted).
- Phase 3: 14 epochs GRPO on the hinted dataset.
- Total 15 epochs, matched budget/config with the plain baseline.
Result (hardset validation)
| metric | untrained | baseline02 (15ep plain) | HINT (this model) |
|---|---|---|---|
| acc mean@4 | 0.026 | 0.067 | 0.0865 |
| acc best@4 | 0.046 | — | 0.122 |
Equal-budget improvement of +29% mean@4 over the plain baseline; validation accuracy increased monotonically over training.
Training config
- 2 nodes x 8 GPU, TP=1, GRPO, lr=1e-6 (constant), train_batch=128, n=8, temperature=1.5, repetition_penalty=1.05
- max_prompt_len=2048, max_response_len=16384, attn=sdpa
Note: single-seed run; multi-seed variance not yet measured.