[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KQK8F2BMD1FTGP9Z6YXK07":3,"skill-same:01M6KQK8F2BMD1FTGP9Z6YXK07":85},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":13,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":60,"install":61,"resources":74},"01M6KQK8F2BMD1FTGP9Z6YXK07","miles-rl-training",null,"Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8\u002FINT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.","endman100\u002Fskill-router-nexus","endman100","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F31997019?v=4","01M6KPDX17BYDWW4RRB0TNGGMX","01M6KPDX64Y2QBEWVFG5W5PWPF",0,"MIT","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAI-Training\u002Fmiles\u002Fmiles\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"miles","miles rl training","rl","training","alignment","when","for","orchestra","production-ready","throughput","Megatron-LM","megatron","slime","ready","router","use","learning","Transformers","SGLang","Enterprise","FP8","2026-11-10 10:12:30",false,"---\nname: miles-rl-training\ndescription: Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8\u002FINT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Reinforcement Learning, MoE, FP8, INT4, Enterprise, SGLang, Megatron-LM]\ndependencies: [sglang-router>=0.2.3, ray, torch>=2.0.0, transformers>=4.40.0]\n---\n\n# miles: Enterprise-Grade RL for Large-Scale Model Training\n\nmiles is a high-performance, enterprise-ready RL framework optimized for large-scale model post-training. Built as a production fork of slime, it addresses critical challenges in MoE training stability, low-precision training, and train-inference alignment.\n\n## When to Use miles\n\n**Choose miles when you need:**\n- Training 1TB+ MoE models (DeepSeek V3, Qwen3-MoE)\n- FP8 or INT4 quantization-aware training\n- Bit-wise identical train-inference alignment\n- Speculative RL for maximum throughput\n- Production stability with enterprise support\n\n**Consider alternatives when:**\n- You want the research-grade original → use **slime**\n- You need flexible backend swapping → use **verl**\n- You want PyTorch-native abstractions → use **torchforge**\n\n## Key Features\n\n### Low-Precision Training\n- **Unified FP8**: End-to-end FP8 for both inference and training\n- **INT4 QAT**: 1TB models on single-machine VRAM (H200)\n- **Rollout Routing Replay (R3)**: Bit-wise expert alignment for MoE\n\n### Performance Optimizations\n- **Speculative RL**: 25%+ rollout speedup with online SFT draft models\n- **Zero-Copy Weight Sync**: CUDA IPC zero-copy mapping\n- **Partial Rollout**: Recycle half-finished trajectories\n\n### Train-Inference Alignment\n- **TIS\u002FMIS**: Truncated\u002FMasked Importance Sampling for off-policy correction\n- **Kernel-level optimization**: FlashAttention-3, DeepGEMM integration\n\n## Installation\n\n```bash\n# Recommended: Docker\ndocker pull radixark\u002Fmiles:latest\ndocker run --rm --gpus all --ipc=host --shm-size=16g \\\n  -it radixark\u002Fmiles:latest \u002Fbin\u002Fbash\n\n# From source\ngit clone https:\u002F\u002Fgithub.com\u002Fradixark\u002Fmiles.git\ncd miles\npip install -r requirements.txt\npip install -e .\n```\n\n## Quick Start\n\nmiles inherits slime's configuration system. Basic training:\n\n```bash\npython train.py \\\n    --advantage-estimator grpo \\\n    --model-name qwen3-30b-a3b \\\n    --hf-checkpoint \u002Fpath\u002Fto\u002Fqwen3-30b-a3b-hf \\\n    --rollout-batch-size 512 \\\n    --n-samples-per-prompt 8\n```\n\n---\n\n## Workflow 1: Large MoE Training\n\nUse this workflow for training large MoE models like DeepSeek V3 or Qwen3-MoE.\n\n### Prerequisites Checklist\n- [ ] H100\u002FH200 GPUs with FP8 support\n- [ ] MoE model (DeepSeek V3, Qwen3-MoE)\n- [ ] Docker environment with miles\n\n### Step 1: Environment Setup\n\n```bash\n# FP8 block scaling (recommended for stability)\nexport NVTE_FP8_BLOCK_SCALING_FP32_SCALES=1\nexport CUDA_DEVICE_MAX_CONNECTIONS=1\n```\n\n### Step 2: Configure Training\n\n```bash\npython train.py \\\n    --actor-num-gpus-per-node 8 \\\n    --rollout-num-gpus 8 \\\n    --hf-checkpoint \u002Fpath\u002Fto\u002Fdeepseek-v3 \\\n    --advantage-estimator grpo \\\n    --tensor-model-parallel-size 8 \\\n    --expert-model-parallel-size 4 \\\n    --prompt-data \u002Fpath\u002Fto\u002Fdata.jsonl \\\n    --num-rollout 3000\n```\n\n### Verification Checklist\n- [ ] Model loads without errors\n- [ ] Routing decisions are consistent\n- [ ] No NaN\u002FInf in loss values\n\n---\n\n## Workflow 2: Speculative RL Training\n\nUse this workflow for maximum rollout throughput with EAGLE speculative decoding.\n\n### How Speculative RL Works\n\n1. Small draft model generates candidate tokens\n2. Target model verifies in parallel\n3. Draft model updated via online SFT to track policy\n\n### Step 1: Enable Speculative Decoding\n\nmiles supports EAGLE speculative decoding via SGLang:\n\n```bash\npython train.py \\\n    --actor-num-gpus-per-node 8 \\\n    --hf-checkpoint \u002Fpath\u002Fto\u002Ftarget-model \\\n    --sglang-speculative-algorithm EAGLE \\\n    --sglang-speculative-num-steps 3 \\\n    --sglang-speculative-eagle-topk 1 \\\n    --sglang-speculative-num-draft-tokens 4 \\\n    --sglang-speculative-draft-model-path \u002Fpath\u002Fto\u002Fdraft-model \\\n    --advantage-estimator grpo \\\n    --prompt-data \u002Fpath\u002Fto\u002Fdata.jsonl\n```\n\n### Step 2: Enable Online MTP Training (Optional)\n\nFor online SFT of draft model during training:\n\n```bash\n--mtp-num-layers 1 \\\n--enable-mtp-training \\\n--mtp-loss-scaling-factor 0.2\n```\n\n**Note**: Online MTP training requires a torch dist checkpoint with MTP weights. Add `--mtp-num-layers 1` during checkpoint conversion from HuggingFace.\n\n### Expected Speedup\n\n- **Standard rollout**: Baseline\n- **Speculative RL**: 25-40% faster rollout\n- **With partial rollout**: Additional 10-15% throughput\n\n---\n\n## Configuration Reference\n\nmiles inherits all slime arguments. See [slime API Reference](..\u002Fslime\u002Freferences\u002Fapi-reference.md) for the complete list.\n\n### Cluster Resources (from slime)\n\n```bash\n--actor-num-nodes 1\n--actor-num-gpus-per-node 8\n--rollout-num-gpus 8\n--rollout-num-gpus-per-engine 2\n--colocate\n```\n\n### Megatron Parallelism (from slime)\n\n```bash\n--tensor-model-parallel-size 8\n--pipeline-model-parallel-size 2\n--expert-model-parallel-size 4    # MoE expert parallelism\n```\n\n### Speculative Decoding (miles-specific)\n\n```bash\n--sglang-speculative-algorithm EAGLE\n--sglang-speculative-num-steps 3\n--sglang-speculative-eagle-topk 1\n--sglang-speculative-num-draft-tokens 4\n--sglang-enable-draft-weights-cpu-backup\n--sglang-speculative-draft-model-path \u002Fyour\u002Fdraft\u002Fmodel\u002Fpath\n```\n\n### Online MTP Training (miles-specific)\n\n```bash\n--mtp-num-layers 1\n--enable-mtp-training\n--mtp-loss-scaling-factor 0.2\n```\n\n---\n\n## Key Features (Conceptual)\n\nThe following features are documented in miles but specific CLI flags may vary. Consult the miles repository for latest configuration.\n\n### Unified FP8 Pipeline\n\nEnd-to-end FP8 sampling and training that eliminates quantization-induced discrepancy causing RL collapse in MoE models.\n\n### Rollout Routing Replay (R3)\n\nRecords expert routing decisions during SGLang inference and replays them during Megatron training for bit-wise expert alignment.\n\n**How R3 Works**:\n1. During SGLang inference, expert routing decisions are recorded\n2. Routing decisions stored in `sample.rollout_routed_experts`\n3. During Megatron training, routing is replayed instead of recomputed\n4. Ensures identical expert selection between train and inference\n\n### INT4 Quantization-Aware Training\n\nEnables single-machine deployment of 1TB+ models (e.g., on H200).\n\n**Memory Savings with INT4**:\n\n| Model Size | BF16 VRAM | INT4 VRAM | Reduction |\n|------------|-----------|-----------|-----------|\n| 70B | 140GB | 45GB | 3.1x |\n| 235B | 470GB | 150GB | 3.1x |\n| 671B | 1.3TB | 420GB | 3.1x |\n\n### Train-Inference Alignment\n\nmiles achieves \"exactly 0 KL divergence\" between training and inference through:\n- Flash Attention 3\n- DeepGEMM\n- Batch-invariant kernels from Thinking Machines Lab\n- `torch.compile` integration\n\n---\n\n## Sample Data Structure\n\nmiles uses the same `Sample` dataclass as slime with the `rollout_routed_experts` field for MoE routing replay:\n\n```python\n@dataclass\nclass Sample:\n    prompt: str | list[dict]\n    tokens: list[int]\n    response: str\n    reward: float | dict\n    loss_mask: list[int]\n    status: Status\n    metadata: dict\n    rollout_log_probs: list[float]\n    rollout_routed_experts: list[list[int]]  # MoE routing for R3\n```\n\nSee [slime API Reference](..\u002Fslime\u002Freferences\u002Fapi-reference.md) for the complete Sample definition.\n\n---\n\n## Common Issues and Solutions\n\n### Issue: FP8 Training Collapse\n\n**Symptoms**: Loss explodes, NaN values\n\n**Solutions**:\n- Use block scaling: `export NVTE_FP8_BLOCK_SCALING_FP32_SCALES=1`\n- Reduce learning rate: `--lr 5e-7`\n- Ensure MoE routing is consistent between train\u002Finference\n\n### Issue: Speculative Draft Drift\n\n**Symptoms**: Low acceptance rate over time\n\n**Solutions**:\n- Enable online MTP training to keep draft model aligned\n- Reduce speculative steps: `--sglang-speculative-num-steps 2`\n- Use CPU backup: `--sglang-enable-draft-weights-cpu-backup`\n\n### Issue: Train-Inference Mismatch\n\n**Symptoms**: Policy divergence, reward collapse\n\n**Solutions**:\n- Use TIS for off-policy correction: `--use-tis --tis-threshold 0.9`\n- Verify log probs match between SGLang and Megatron\n- Enable R3 for MoE models\n\n---\n\n## Supported Models\n\n| Family | Models | MoE Support |\n|--------|--------|-------------|\n| DeepSeek | R1, V3, V3.2 | Full |\n| Qwen | 2, 2.5, 3 (including MoE) | Full |\n| Llama | 3, 3.1, 3.3, 4 | Dense only |\n| Gemma | 2, 3, 3N | Dense only |\n| GLM | 4.5, 4.6, 4.7 | Dense only |\n| MiniMax | M2, M2.1 | Full |\n\n---\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fradixark\u002Fmiles\n- **Introduction Blog**: https:\u002F\u002Flmsys.org\u002Fblog\u002F2025-11-19-miles\u002F\n- **Slime (upstream)**: https:\u002F\u002Fgithub.com\u002FTHUDM\u002Fslime\n- **SGLang**: https:\u002F\u002Fgithub.com\u002Fsgl-project\u002Fsglang\n\n","# miles: Enterprise-Grade RL for Large-Scale Model Training\n\nmiles is a high-performance, enterprise-ready RL framework optimized for large-scale model post-training. Built as a production fork of slime, it addresses critical challenges in MoE training stability, low-precision training, and train-inference alignment.\n\n## When to Use miles\n\n**Choose miles when you need:**\n- Training 1TB+ MoE models (DeepSeek V3, Qwen3-MoE)\n- FP8 or INT4 quantization-aware training\n- Bit-wise identical train-inference alignment\n- Speculative RL for maximum throughput\n- Production stability with enterprise support\n\n**Consider alternatives when:**\n- You want the research-grade original → use **slime**\n- You need flexible backend swapping → use **verl**\n- You want PyTorch-native abstractions → use **torchforge**\n\n## Key Features\n\n### Low-Precision Training\n- **Unified FP8**: End-to-end FP8 for both inference and training\n- **INT4 QAT**: 1TB models on single-machine VRAM (H200)\n- **Rollout Routing Replay (R3)**: Bit-wise expert alignment for MoE\n\n### Performance Optimizations\n- **Speculative RL**: 25%+ rollout speedup with online SFT draft models\n- **Zero-Copy Weight Sync**: CUDA IPC zero-copy mapping\n- **Partial Rollout**: Recycle half-finished trajectories\n\n### Train-Inference Alignment\n- **TIS\u002FMIS**: Truncated\u002FMasked Importance Sampling for off-policy correction\n- **Kernel-level optimization**: FlashAttention-3, DeepGEMM integration\n\n## Installation\n\n```bash\n# Recommended: Docker\ndocker pull radixark\u002Fmiles:latest\ndocker run --rm --gpus all --ipc=host --shm-size=16g \\\n  -it radixark\u002Fmiles:latest \u002Fbin\u002Fbash\n\n# From source\ngit clone https:\u002F\u002Fgithub.com\u002Fradixark\u002Fmiles.git\ncd miles\npip install -r requirements.txt\npip install -e .\n```\n\n## Quick Start\n\nmiles inherits slime's configuration system. Basic training:\n\n```bash\npython train.py \\\n    --advantage-estimator grpo \\\n    --model-name qwen3-30b-a3b \\\n    --hf-checkpoint \u002Fpath\u002Fto\u002Fqwen3-30b-a3b-hf \\\n    --rollout-batch-size 512 \\\n    --n-samples-per-prompt 8\n```\n\n---\n\n## Workflow 1: Large MoE Training\n\nUse this workflow for training large MoE models like DeepSeek V3 or Qwen3-MoE.\n\n### Prerequisites Checklist\n- [ ] H100\u002FH200 GPUs with FP8 support\n- [ ] MoE model (DeepSeek V3, Qwen3-MoE)\n- [ ] Docker environment with miles\n\n### Step 1: Environment Setup\n\n```bash\n# FP8 block scaling (recommended for stability)\nexport NVTE_FP8_BLOCK_SCALING_FP32_SCALES=1\nexport CUDA_DEVICE_MAX_CONNECTIONS=1\n```\n\n### Step 2: Configure Training\n\n```bash\npython train.py \\\n    --actor-num-gpus-per-node 8 \\\n    --rollout-num-gpus 8 \\\n    --hf-checkpoint \u002Fpath\u002Fto\u002Fdeepseek-v3 \\\n    --advantage-estimator grpo \\\n    --tensor-model-parallel-size 8 \\\n    --expert-model-parallel-size 4 \\\n    --prompt-data \u002Fpath\u002Fto\u002Fdata.jsonl \\\n    --num-rollout 3000\n```\n\n### Verification Checklist\n- [ ] Model loads without errors\n- [ ] Routing decisions are consistent\n- [ ] No NaN\u002FInf in loss values\n\n---\n\n## Workflow 2: Speculative RL Training\n\nUse this workflow for maximum rollout throughput with EAGLE speculative decoding.\n\n### How Speculative RL Works\n\n1. Small draft model generates candidate tokens\n2. Target model verifies in parallel\n3. Draft model updated via online SFT to track policy\n\n### Step 1: Enable Speculative Decoding\n\nmiles supports EAGLE speculative decoding via SGLang:\n\n```bash\npython train.py \\\n    --actor-num-gpus-per-node 8 \\\n    --hf-checkpoint \u002Fpath\u002Fto\u002Ftarget-model \\\n    --sglang-speculative-algorithm EAGLE \\\n    --sglang-speculative-num-steps 3 \\\n    --sglang-speculative-eagle-topk 1 \\\n    --sglang-speculative-num-draft-tokens 4 \\\n    --sglang-speculative-draft-model-path \u002Fpath\u002Fto\u002Fdraft-model \\\n    --advantage-estimator grpo \\\n    --prompt-data \u002Fpath\u002Fto\u002Fdata.jsonl\n```\n\n### Step 2: Enable Online MTP Training (Optional)\n\nFor online SFT of draft model during training:\n\n```bash\n--mtp-num-layers 1 \\\n--enable-mtp-training \\\n--mtp-loss-scaling-factor 0.2\n```\n\n**Note**: Online MTP training requires a torch dist checkpoint with MTP weights. Add `--mtp-num-layers 1` during checkpoint conversion from HuggingFace.\n\n### Expected Speedup\n\n- **Standard rollout**: Baseline\n- **Speculative RL**: 25-40% faster rollout\n- **With partial rollout**: Additional 10-15% throughput\n\n---\n\n## Configuration Reference\n\nmiles inherits all slime arguments. See [slime API Reference](..\u002Fslime\u002Freferences\u002Fapi-reference.md) for the complete list.\n\n### Cluster Resources (from slime)\n\n```bash\n--actor-num-nodes 1\n--actor-num-gpus-per-node 8\n--rollout-num-gpus 8\n--rollout-num-gpus-per-engine 2\n--colocate\n```\n\n### Megatron Parallelism (from slime)\n\n```bash\n--tensor-model-parallel-size 8\n--pipeline-model-parallel-size 2\n--expert-model-parallel-size 4    # MoE expert parallelism\n```\n\n### Speculative Decoding (miles-specific)\n\n```bash\n--sglang-speculative-algorithm EAGLE\n--sglang-speculative-num-steps 3\n--sglang-speculative-eagle-topk 1\n--sglang-speculative-num-draft-tokens 4\n--sglang-enable-draft-weights-cpu-backup\n--sglang-speculative-draft-model-path \u002Fyour\u002Fdraft\u002Fmodel\u002Fpath\n```\n\n### Online MTP Training (miles-specific)\n\n```bash\n--mtp-num-layers 1\n--enable-mtp-training\n--mtp-loss-scaling-factor 0.2\n```\n\n---\n\n## Key Features (Conceptual)\n\nThe following features are documented in miles but specific CLI flags may vary. Consult the miles repository for latest configuration.\n\n### Unified FP8 Pipeline\n\nEnd-to-end FP8 sampling and training that eliminates quantization-induced discrepancy causing RL collapse in MoE models.\n\n### Rollout Routing Replay (R3)\n\nRecords expert routing decisions during SGLang inference and replays them during Megatron training for bit-wise expert alignment.\n\n**How R3 Works**:\n1. During SGLang inference, expert routing decisions are recorded\n2. Routing decisions stored in `sample.rollout_routed_experts`\n3. During Megatron training, routing is replayed instead of recomputed\n4. Ensures identical expert selection between train and inference\n\n### INT4 Quantization-Aware Training\n\nEnables single-machine deployment of 1TB+ models (e.g., on H200).\n\n**Memory Savings with INT4**:\n\n| Model Size | BF16 VRAM | INT4 VRAM | Reduction |\n|------------|-----------|-----------|-----------|\n| 70B | 140GB | 45GB | 3.1x |\n| 235B | 470GB | 150GB | 3.1x |\n| 671B | 1.3TB | 420GB | 3.1x |\n\n### Train-Inference Alignment\n\nmiles achieves \"exactly 0 KL divergence\" between training and inference through:\n- Flash Attention 3\n- DeepGEMM\n- Batch-invariant kernels from Thinking Machines Lab\n- `torch.compile` integration\n\n---\n\n## Sample Data Structure\n\nmiles uses the same `Sample` dataclass as slime with the `rollout_routed_experts` field for MoE routing replay:\n\n```python\n@dataclass\nclass Sample:\n    prompt: str | list[dict]\n    tokens: list[int]\n    response: str\n    reward: float | dict\n    loss_mask: list[int]\n    status: Status\n    metadata: dict\n    rollout_log_probs: list[float]\n    rollout_routed_experts: list[list[int]]  # MoE routing for R3\n```\n\nSee [slime API Reference](..\u002Fslime\u002Freferences\u002Fapi-reference.md) for the complete Sample definition.\n\n---\n\n## Common Issues and Solutions\n\n### Issue: FP8 Training Collapse\n\n**Symptoms**: Loss explodes, NaN values\n\n**Solutions**:\n- Use block scaling: `export NVTE_FP8_BLOCK_SCALING_FP32_SCALES=1`\n- Reduce learning rate: `--lr 5e-7`\n- Ensure MoE routing is consistent between train\u002Finference\n\n### Issue: Speculative Draft Drift\n\n**Symptoms**: Low acceptance rate over time\n\n**Solutions**:\n- Enable online MTP training to keep draft model aligned\n- Reduce speculative steps: `--sglang-speculative-num-steps 2`\n- Use CPU backup: `--sglang-enable-draft-weights-cpu-backup`\n\n### Issue: Train-Inference Mismatch\n\n**Symptoms**: Policy divergence, reward collapse\n\n**Solutions**:\n- Use TIS for off-policy correction: `--use-tis --tis-threshold 0.9`\n- Verify log probs match between SGLang and Megatron\n- Enable R3 for MoE models\n\n---\n\n## Supported Models\n\n| Family | Models | MoE Support |\n|--------|--------|-------------|\n| DeepSeek | R1, V3, V3.2 | Full |\n| Qwen | 2, 2.5, 3 (including MoE) | Full |\n| Llama | 3, 3.1, 3.3, 4 | Dense only |\n| Gemma | 2, 3, 3N | Dense only |\n| GLM | 4.5, 4.6, 4.7 | Dense only |\n| MiniMax | M2, M2.1 | Full |\n\n---\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fradixark\u002Fmiles\n- **Introduction Blog**: https:\u002F\u002Flmsys.org\u002Fblog\u002F2025-11-19-miles\u002F\n- **Slime (upstream)**: https:\u002F\u002Fgithub.com\u002FTHUDM\u002Fslime\n- **SGLang**: https:\u002F\u002Fgithub.com\u002Fsgl-project\u002Fsglang\n\n",{"name":5,"tags":44,"author":48,"license":14,"version":49,"description":7,"dependencies":50},[45,46,38,47,37,36,28],"Reinforcement Learning","MoE","INT4","Orchestra 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