111 lines
5.1 KiB
Markdown
111 lines
5.1 KiB
Markdown
---
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- qwen3
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- reinforcement-learning
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- grpo
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- text-generation
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base_model: Qwen/Qwen3-8B
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---
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# Qwen3-8B-Physics-GRPO-TR
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This repository contains the Qwen3-8B `physics` `GRPO` batch-size-32 run. The repository name uses the project `GRPO-TR` naming convention, but the actual training method for this checkpoint is GRPO.
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The repository root contains the best validation checkpoint, selected by validation `mean@16`. `checkpoints/last/` contains the final checkpoint.
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## Performance
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| Dataset | Method | Base model | Train batch size | Best val mean@16 | Best checkpoint | Final val mean@16 | Final checkpoint |
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|---|---|---|---:|---:|---:|---:|---:|
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| Physics / SciKnowEval physics | GRPO | Qwen3-8B | 32 | 72.97% | 100 | 72.97% | 100 |
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## Validation Mean@16
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| step | val_mean16 | percent |
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|---:|---:|---:|
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| 10 | 0.583593750000 | 58.36% |
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| 20 | 0.599218750000 | 59.92% |
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| 30 | 0.604687500000 | 60.47% |
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| 40 | 0.621093750000 | 62.11% |
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| 50 | 0.653906250000 | 65.39% |
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| 60 | 0.671875000000 | 67.19% |
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| 70 | 0.681250000000 | 68.12% |
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| 80 | 0.712500000000 | 71.25% |
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| 90 | 0.722656250000 | 72.27% |
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| 100 | 0.729687500000 | 72.97% |
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## Detailed Training Hyperparameters
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| Section | Parameter | Value | Source |
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|---|---|---:|---|
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| Run identity | `Base model` | `Qwen/Qwen3-8B` | queue/script override |
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| Run identity | `Dataset` | `Physics / SciKnowEval physics` | run_qwen3_generalization.sh |
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| Run identity | `Method` | `GRPO` | run_qwen3_generalization.sh |
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| Run identity | `Config` | `baseline_grpo` | run_qwen3_generalization.sh |
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| Run identity | `Experiment` | `qwen3gen-physics-GRPO-Qwen-Qwen3-8B-mbs8-train32-rollout8-lr1e-6-vllm0.8` | run_qwen3_generalization.sh |
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| Run identity | `W&B run` | `run-20260703_032040-6ig3l55l` | wandb |
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| Data | `Train file` | `datasets/sciknoweval/physics/train.parquet` | script override |
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| Data | `Validation file` | `datasets/sciknoweval/physics/test.parquet` | script override |
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| Data | `Train batch size` | `32` | queue/script override |
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| Data | `Train max samples` | `3200` | queue/script override |
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| Schedule | `Total training steps` | `100` | queue/script override |
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| Schedule | `Validation before train` | `False` | queue/script override |
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| Schedule | `Save frequency` | `10` | queue/script override |
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| Schedule | `Validation frequency` | `10` | queue/script override |
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| Sequence | `Max prompt length` | `2048` | queue/script override |
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| Sequence | `Max response length` | `8192` | queue/script override |
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| Sequence | `Max model length` | `10240` | queue/script override |
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| Rollout | `Train rollout n` | `8` | queue/script override |
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| Rollout | `Validation rollout n` | `16` | queue/script override |
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| Rollout | `vLLM GPU memory utilization` | `0.8` | queue/script override |
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| Optimization | `Learning rate` | `1e-6` | GRPO method override |
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| Optimization | `Weight decay` | `0.01` | script override |
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| PPO/GRPO | `PPO mini batch size` | `8` | queue/script override |
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| PPO/GRPO | `Normalize GRPO advantages by std` | `False` | baseline_grpo.yaml / script override |
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| Rollout correction | `Importance sampling mode` | `token` | script override |
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| Rollout correction | `IS threshold` | `2.0` | script override |
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| Checkpoint/Logging | `Checkpoint root` | `checkpoints/datasets/sciknoweval/physics/qwen3gen-physics-GRPO-Qwen-Qwen3-8B-mbs8-train32-rollout8-lr1e-6-vllm0.8` | script override |
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| Checkpoint/Logging | `Latest checkpointed iteration` | `100` | latest_checkpointed_iteration.txt |
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| Checkpoint/Logging | `External actor archive` | `checkpoints/datasets/sciknoweval/physics/qwen3gen-physics-GRPO-Qwen-Qwen3-8B-mbs8-train32-rollout8-lr1e-6-vllm0.8/_actor_archive` | preserve_actor_checkpoints.py |
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| Checkpoint/Logging | `Logger` | `console, wandb` | ppo_trainer.yaml |
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| PPO/GRPO | `Policy loss mode` | `vanilla` | method override |
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| PPO/GRPO | `Actor KL loss coef` | `0.0` | method override |
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Raw result and artifact files:
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- `results/validation_mean16.csv`
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- `results/training_scores.csv`
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- `results/hyperparameters.csv`
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- `results/training_score.png`
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- `results/training_score.svg`
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- `artifacts/output.log`
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- `artifacts/queue.log`
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo_id = "SeongryongJung/Qwen3-8B-Physics-GRPO-TR"
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tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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repo_id,
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torch_dtype="auto",
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device_map="auto",
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trust_remote_code=True,
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)
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```
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## Source
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- Checkpoint: `checkpoints/datasets/sciknoweval/physics/qwen3gen-physics-GRPO-Qwen-Qwen3-8B-mbs8-train32-rollout8-lr1e-6-vllm0.8`
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- Root actor checkpoint: `checkpoints/datasets/sciknoweval/physics/qwen3gen-physics-GRPO-Qwen-Qwen3-8B-mbs8-train32-rollout8-lr1e-6-vllm0.8/global_step_100/actor`
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- Last actor checkpoint: `checkpoints/datasets/sciknoweval/physics/qwen3gen-physics-GRPO-Qwen-Qwen3-8B-mbs8-train32-rollout8-lr1e-6-vllm0.8/global_step_100/actor`
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- W&B run: `run-20260703_032040-6ig3l55l`
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- Queue log: `artifacts/queue.log`
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