| # Baselines |
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| Environment: [hiyouga/verl:ngc-th2.6.0-cu126-vllm0.8.3-flashinfer0.2.2-cxx11abi0](https://hub.docker.com/layers/hiyouga/verl/ngc-th2.6.0-cu126-vllm0.8.3-flashinfer0.2.2-cxx11abi0/images/sha256-335ed6cd1fe73090e458409cfa4394d6abf4cd0503ca44dbafdc28ff72e5ed20) |
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| EasyR1 version: [v0.3.0](https://github.com/hiyouga/EasyR1/tree/v0.3.0) |
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| Welcome to contribute new data points! |
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| ## Algorithm Baselines |
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| ### [Qwen2.5-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on [Math12k](https://huggingface.co/datasets/hiyouga/math12k) |
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| | Size | Algorithm | Bits | LR | KL | Test Score | |
| | ---- | ----------- | ---- | ---- | ---- | ---------- | |
| | 7B | GRPO | AMP | 1e-6 | 1e-2 | 0.73->0.79 | |
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| ### [Qwen2.5-VL-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) on [Geometry3k](https://huggingface.co/datasets/hiyouga/geometry3k) |
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| | Size | Algorithm | Bits | LR | KL | Test Score | |
| | ---- | ----------- | ---- | ---- | ---- | ---------- | |
| | 7B | GRPO | AMP | 1e-6 | 1e-2 | 0.39->0.52 | |
| | 7B | GRPO | BF16 | 1e-6 | 1e-2 | 0.39->0.52 | |
| | 7B | GRPO | AMP | 1e-6 | 1e-3 | 0.39->0.52 | |
| | 7B | RLOO | AMP | 1e-6 | 1e-2 | 0.39->0.53 | |
| | 3B | GRPO | AMP | 1e-6 | 1e-2 | 0.27->0.44 | |
| | 32B | GRPO | BF16 | 1e-6 | 1e-2 | 0.46->0.61 | |
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| > [!NOTE] |
| > The hyper-parameters not listed are all the same as the default values. |
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| ## Performance Baselines |
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| ### [Qwen2.5-VL-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) on [Geometry3k](https://huggingface.co/datasets/hiyouga/geometry3k) |
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| | Size | GPU Type | Bits | Batch Size | vLLM Util | vLLM TP | Peak Mem | Peak VRAM | Throughput | Sec per step | Actor MFU | |
| | ---- | ------------- | ---- | ---------- | --------- | ------- | -------- | --------- | ---------- | ------------ | --------- | |
| | 3B | 8 * H100 80GB | AMP | 4 / 16 | 0.6 | 2 | 120GB | 35GB | 1200 | 180s | 6.3% | |
| | 7B | 8 * H100 80GB | AMP | 4 / 16 | 0.6 | 2 | 140GB | 60GB | 1200 | 180s | 13.6% | |
| | 7B | 8 * H100 80GB | AMP | 10 / 20 | 0.6 | 2 | 150GB | 75GB | 1400 | 170s | 19.2% | |
| | 7B | 8 * L20 48GB | AMP | 4 / 16 | 0.6 | 2 | 150GB | 44GB | 410 | 580s | 26.5% | |
| | 7B | 8 * H100 80GB | BF16 | 4 / 16 | 0.6 | 2 | 150GB | 50GB | 1280 | 190s | 13.9% | |
| | 32B | 8 * H100 80GB | BF16 | 1 / 8 | 0.6 | 8 | 240GB | 68GB | 360 | 860s | 11.2% | |
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| - Batch Size: micro_batch_size_per_device_for_update / micro_batch_size_per_device_for_experience |
| - vLLM Util: rollout.gpu_memory_utilization |
| - vLLM TP: rollout.tensor_parallel_size |
| - Peak Mem: Peak CPU memory usage |
| - Peak VRAM: Peak GPU memory usage |
| - Throughput: Number of tokens per second per GPU by one training step |
| - Sec per step: Average time per step in seconds |
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| > [!NOTE] |
| > The hyper-parameters not listed are all the same as the default values. |
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