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Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-torchforge\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"training","torchforge","post training torchforge","post","clean","when","for","orchestra","you","native","Scalable","experimentation","algorithms","infra","agentic","use","learning","Meta","vLLM","GRPO","library","reinforcement learning","pytorch","research","2026-11-10 10:12:30",false,"---\nname: post-training-torchforge\ndescription: Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Orchestra Research\n  tags: [Reinforcement Learning, PyTorch, GRPO, SFT, Monarch, TorchTitan, Meta]\n  dependencies: [torch>=2.9.0, torchtitan>=0.2.0, vllm, monarch]\n---\n\n# torchforge: PyTorch-Native Agentic RL Library\n\ntorchforge is Meta's PyTorch-native RL library that separates infrastructure concerns from algorithm concerns. It enables rapid RL research by letting you focus on algorithms while handling distributed training, inference, and weight sync automatically.\n\n## When to Use torchforge\n\n**Choose torchforge when you need:**\n- Clean separation between RL algorithms and infrastructure\n- PyTorch-native abstractions (no Ray dependency)\n- Easy algorithm experimentation (GRPO, DAPO, SAPO in ~100 lines)\n- Scalable training with Monarch actor system\n- Integration with TorchTitan for model parallelism\n\n**Consider alternatives when:**\n- You need production-ready stability → use **miles** or **verl**\n- You want Megatron-native training → use **slime**\n- torchforge is experimental and APIs may change\n\n## Key Features\n\n- **Algorithm isolation**: Implement RL algorithms without touching infrastructure\n- **Scalability**: From single GPU to thousands via Monarch\n- **Modern stack**: TorchTitan (training), vLLM (inference), TorchStore (sync)\n- **Loss functions**: GRPO, DAPO, CISPO, GSPO, SAPO built-in\n\n## Architecture Overview\n\n```\n┌─────────────────────────────────────────────────────────┐\n│ Application Layer (Your Code)                           │\n│ - Define reward models, loss functions, sampling        │\n└─────────────────────┬───────────────────────────────────┘\n                      │\n┌─────────────────────▼───────────────────────────────────┐\n│ Forge API Layer                                         │\n│ - Episode, Group dataclasses                           │\n│ - Service interfaces (async\u002Fawait)                      │\n└─────────────────────┬───────────────────────────────────┘\n                      │\n┌─────────────────────▼───────────────────────────────────┐\n│ Distributed Services (Monarch)                          │\n│ ├── Trainer (TorchTitan FSDP)                          │\n│ ├── Generator (vLLM inference)                          │\n│ ├── Reference Model (frozen KL baseline)               │\n│ └── Reward Actors (compute rewards)                    │\n└─────────────────────────────────────────────────────────┘\n```\n\n## Installation\n\n```bash\n# Create environment\nconda create -n forge python=3.12\nconda activate forge\n\n# Install (handles PyTorch nightly + dependencies)\n.\u002Fscripts\u002Finstall.sh\n\n# Verify\npython -c \"import torch, forge, vllm; print('OK')\"\n```\n\n### ROCm Installation\n\n```bash\n.\u002Fscripts\u002Finstall_rocm.sh\n```\n\n## Quick Start\n\n### SFT Training (2+ GPUs)\n\n```bash\npython -m apps.sft.main --config apps\u002Fsft\u002Fllama3_8b.yaml\n```\n\n### GRPO Training (3+ GPUs)\n\n```bash\npython -m apps.grpo.main --config apps\u002Fgrpo\u002Fqwen3_1_7b.yaml\n```\n\n---\n\n## Workflow 1: GRPO Training for Math Reasoning\n\nUse this workflow for training reasoning models with group-relative advantages.\n\n### Prerequisites Checklist\n- [ ] 3+ GPUs (GPU0: trainer, GPU1: ref_model, GPU2: generator)\n- [ ] Model from HuggingFace Hub\n- [ ] Training dataset (GSM8K, MATH, etc.)\n\n### Step 1: Create Configuration\n\n```yaml\n# config\u002Fgrpo_math.yaml\nmodel: \"Qwen\u002FQwen2.5-7B-Instruct\"\n\ndataset:\n  path: \"openai\u002Fgsm8k\"\n  split: \"train\"\n  streaming: true\n\ntraining:\n  batch_size: 4\n  learning_rate: 1e-6\n  seq_len: 4096\n  dtype: bfloat16\n  gradient_accumulation_steps: 4\n\ngrpo:\n  n_samples: 8           # Responses per prompt\n  clip_low: 0.2\n  clip_high: 0.28\n  beta: 0.1              # KL penalty coefficient\n  temperature: 0.7\n\nservices:\n  generator:\n    procs: 1\n    num_replicas: 1\n    with_gpus: true\n  trainer:\n    procs: 1\n    num_replicas: 1\n    with_gpus: true\n  ref_model:\n    procs: 1\n    num_replicas: 1\n    with_gpus: true\n```\n\n### Step 2: Define Reward Function\n\n```python\n# rewards.py\n# Reward functions are in forge.data.rewards\nfrom forge.data.rewards import MathReward, ThinkingReward\nimport re\n\n# Or define your own reward function\nclass CustomMathReward:\n    def __call__(self, prompt: str, response: str, target: str) -> float:\n        # Extract answer from response\n        match = re.search(r'\\\\boxed{([^}]+)}', response)\n        if not match:\n            return 0.0\n\n        answer = match.group(1).strip()\n        return 1.0 if answer == target else 0.0\n```\n\n### Step 3: Launch Training\n\n```bash\npython -m apps.grpo.main --config config\u002Fgrpo_math.yaml\n```\n\n### Step 4: Monitor Progress\n- [ ] Check W&B dashboard for loss curves\n- [ ] Verify entropy is decreasing (policy becoming more deterministic)\n- [ ] Monitor KL divergence (should stay bounded)\n\n---\n\n## Workflow 2: Custom Loss Function\n\nUse this workflow to implement new RL algorithms.\n\n### Step 1: Create Loss Class\n\n```python\n# src\u002Fforge\u002Flosses\u002Fcustom_loss.py\nimport torch\nimport torch.nn as nn\n\nclass CustomLoss(nn.Module):\n    def __init__(self, clip_range: float = 0.2, beta: float = 0.1):\n        super().__init__()\n        self.clip_range = clip_range\n        self.beta = beta\n\n    def forward(\n        self,\n        logprobs: torch.Tensor,\n        ref_logprobs: torch.Tensor,\n        advantages: torch.Tensor,\n        padding_mask: torch.Tensor,\n    ) -> torch.Tensor:\n        # Compute importance ratio\n        ratio = torch.exp(logprobs - ref_logprobs)\n\n        # Clipped policy gradient\n        clipped_ratio = torch.clamp(\n            ratio,\n            1 - self.clip_range,\n            1 + self.clip_range\n        )\n        pg_loss = -torch.min(ratio * advantages, clipped_ratio * advantages)\n\n        # KL penalty\n        kl = ref_logprobs - logprobs\n\n        # Apply mask and aggregate\n        masked_loss = (pg_loss + self.beta * kl) * padding_mask\n        loss = masked_loss.sum() \u002F padding_mask.sum()\n\n        return loss\n```\n\n### Step 2: Integrate into Application\n\n```python\n# apps\u002Fcustom\u002Fmain.py\nfrom forge.losses.custom_loss import CustomLoss\n\nloss_fn = CustomLoss(clip_range=0.2, beta=0.1)\n\n# In training loop\nloss = loss_fn(\n    logprobs=logprobs,\n    ref_logprobs=ref_logprobs,\n    advantages=advantages,\n    padding_mask=padding_mask,\n)\n```\n\n---\n\n## Workflow 3: Multi-GPU Distributed Training\n\nUse this workflow for scaling to multiple GPUs or nodes.\n\n### Configuration for Distributed\n\n```yaml\n# config\u002Fdistributed.yaml\nmodel: \"meta-llama\u002FMeta-Llama-3.1-8B-Instruct\"\n\nparallelism:\n  tensor_parallel_degree: 2    # Split model across GPUs\n  pipeline_parallel_degree: 1\n  data_parallel_shard_degree: 2\n\nservices:\n  generator:\n    procs: 2                   # 2 processes for TP=2\n    num_replicas: 1\n    with_gpus: true\n  trainer:\n    procs: 2\n    num_replicas: 1\n    with_gpus: true\n```\n\n### Launch with SLURM\n\n```bash\n# Submit job\nsbatch --nodes=2 --gpus-per-node=8 run_grpo.sh\n```\n\n### Launch Locally (Multi-GPU)\n\n```bash\n# 8 GPU setup\npython -m apps.grpo.main \\\n    --config config\u002Fdistributed.yaml \\\n    --trainer.procs 4 \\\n    --generator.procs 4\n```\n\n---\n\n## Core API Reference\n\n### Training Batch Format\n\ntorchforge uses dictionary-based batches for training:\n\n```python\n# inputs: list of dicts with torch.Tensor values\ninputs = [{\"tokens\": torch.Tensor}]\n\n# targets: list of dicts with training signals\ntargets = [{\n    \"response\": torch.Tensor,\n    \"ref_logprobs\": torch.Tensor,\n    \"advantages\": torch.Tensor,\n    \"padding_mask\": torch.Tensor\n}]\n\n# train_step returns loss as float\nloss = trainer.train_step(inputs, targets)\n```\n\n### Completion\n\nGenerated output from vLLM:\n\n```python\n@dataclass\nclass Completion:\n    text: str              # Generated text\n    token_ids: list[int]   # Token IDs\n    logprobs: list[float]  # Log probabilities\n    metadata: dict         # Custom metadata\n```\n\n---\n\n## Built-in Loss Functions\n\n### Loss Functions\n\nLoss functions are in the `forge.losses` module:\n\n```python\nfrom forge.losses import SimpleGRPOLoss, ReinforceLoss\n\n# SimpleGRPOLoss for GRPO training\nloss_fn = SimpleGRPOLoss(beta=0.1)\n\n# Forward pass\nloss = loss_fn(\n    logprobs=logprobs,\n    ref_logprobs=ref_logprobs,\n    advantages=advantages,\n    padding_mask=padding_mask\n)\n```\n\n### ReinforceLoss\n\n```python\nfrom forge.losses.reinforce_loss import ReinforceLoss\n\n# With optional importance ratio clipping\nloss_fn = ReinforceLoss(clip_ratio=0.2)\n```\n\n---\n\n## Common Issues and Solutions\n\n### Issue: Not Enough GPUs\n\n**Symptoms**: \"Insufficient GPU resources\" error\n\n**Solutions**:\n```yaml\n# Reduce service requirements\nservices:\n  generator:\n    procs: 1\n    with_gpus: true\n  trainer:\n    procs: 1\n    with_gpus: true\n  # Remove ref_model (uses generator weights)\n```\n\nOr use CPU for reference model:\n```yaml\nref_model:\n  with_gpus: false\n```\n\n### Issue: OOM During Generation\n\n**Symptoms**: CUDA OOM in vLLM\n\n**Solutions**:\n```yaml\n# Reduce batch size\ngrpo:\n  n_samples: 4  # Reduce from 8\n\n# Or reduce sequence length\ntraining:\n  seq_len: 2048\n```\n\n### Issue: Slow Weight Sync\n\n**Symptoms**: Long pauses between training and generation\n\n**Solutions**:\n```bash\n# Enable RDMA (if available)\nexport TORCHSTORE_USE_RDMA=1\n\n# Or reduce sync frequency\ntraining:\n  sync_interval: 10  # Sync every 10 steps\n```\n\n### Issue: Policy Collapse\n\n**Symptoms**: Entropy drops to zero, reward stops improving\n\n**Solutions**:\n```yaml\n# Increase KL penalty\ngrpo:\n  beta: 0.2  # Increase from 0.1\n\n# Or add entropy bonus\ntraining:\n  entropy_coef: 0.01\n```\n\n---\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fmeta-pytorch.org\u002Ftorchforge\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fmeta-pytorch\u002Ftorchforge\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002FYsTYBh6PD9\n- **TorchTitan**: https:\u002F\u002Fgithub.com\u002Fpytorch\u002Ftorchtitan\n- **Monarch**: https:\u002F\u002Fgithub.com\u002Fmeta-pytorch\u002Fmonarch\n\n","# torchforge: PyTorch-Native Agentic RL Library\n\ntorchforge is Meta's PyTorch-native RL library that separates infrastructure concerns from algorithm concerns. It enables rapid RL research by letting you focus on algorithms while handling distributed training, inference, and weight sync automatically.\n\n## When to Use torchforge\n\n**Choose torchforge when you need:**\n- Clean separation between RL algorithms and infrastructure\n- PyTorch-native abstractions (no Ray dependency)\n- Easy algorithm experimentation (GRPO, DAPO, SAPO in ~100 lines)\n- Scalable training with Monarch actor system\n- Integration with TorchTitan for model parallelism\n\n**Consider alternatives when:**\n- You need production-ready stability → use **miles** or **verl**\n- You want Megatron-native training → use **slime**\n- torchforge is experimental and APIs may change\n\n## Key Features\n\n- **Algorithm isolation**: Implement RL algorithms without touching infrastructure\n- **Scalability**: From single GPU to thousands via Monarch\n- **Modern stack**: TorchTitan (training), vLLM (inference), TorchStore (sync)\n- **Loss functions**: GRPO, DAPO, CISPO, GSPO, SAPO built-in\n\n## Architecture Overview\n\n```\n┌─────────────────────────────────────────────────────────┐\n│ Application Layer (Your Code)                           │\n│ - Define reward models, loss functions, sampling        │\n└─────────────────────┬───────────────────────────────────┘\n                      │\n┌─────────────────────▼───────────────────────────────────┐\n│ Forge API Layer                                         │\n│ - Episode, Group dataclasses                           │\n│ - Service interfaces (async\u002Fawait)                      │\n└─────────────────────┬───────────────────────────────────┘\n                      │\n┌─────────────────────▼───────────────────────────────────┐\n│ Distributed Services (Monarch)                          │\n│ ├── Trainer (TorchTitan FSDP)                          │\n│ ├── Generator (vLLM inference)                          │\n│ ├── Reference Model (frozen KL baseline)               │\n│ └── Reward Actors (compute rewards)                    │\n└─────────────────────────────────────────────────────────┘\n```\n\n## Installation\n\n```bash\n# Create environment\nconda create -n forge python=3.12\nconda activate forge\n\n# Install (handles PyTorch nightly + dependencies)\n.\u002Fscripts\u002Finstall.sh\n\n# Verify\npython -c \"import torch, forge, vllm; print('OK')\"\n```\n\n### ROCm Installation\n\n```bash\n.\u002Fscripts\u002Finstall_rocm.sh\n```\n\n## Quick Start\n\n### SFT Training (2+ GPUs)\n\n```bash\npython -m apps.sft.main --config apps\u002Fsft\u002Fllama3_8b.yaml\n```\n\n### GRPO Training (3+ GPUs)\n\n```bash\npython -m apps.grpo.main --config apps\u002Fgrpo\u002Fqwen3_1_7b.yaml\n```\n\n---\n\n## Workflow 1: GRPO Training for Math Reasoning\n\nUse this workflow for training reasoning models with group-relative advantages.\n\n### Prerequisites Checklist\n- [ ] 3+ GPUs (GPU0: trainer, GPU1: ref_model, GPU2: generator)\n- [ ] Model from HuggingFace Hub\n- [ ] Training dataset (GSM8K, MATH, etc.)\n\n### Step 1: Create Configuration\n\n```yaml\n# config\u002Fgrpo_math.yaml\nmodel: \"Qwen\u002FQwen2.5-7B-Instruct\"\n\ndataset:\n  path: \"openai\u002Fgsm8k\"\n  split: \"train\"\n  streaming: true\n\ntraining:\n  batch_size: 4\n  learning_rate: 1e-6\n  seq_len: 4096\n  dtype: bfloat16\n  gradient_accumulation_steps: 4\n\ngrpo:\n  n_samples: 8           # Responses per prompt\n  clip_low: 0.2\n  clip_high: 0.28\n  beta: 0.1              # KL penalty coefficient\n  temperature: 0.7\n\nservices:\n  generator:\n    procs: 1\n    num_replicas: 1\n    with_gpus: true\n  trainer:\n    procs: 1\n    num_replicas: 1\n    with_gpus: true\n  ref_model:\n    procs: 1\n    num_replicas: 1\n    with_gpus: true\n```\n\n### Step 2: Define Reward Function\n\n```python\n# rewards.py\n# Reward functions are in forge.data.rewards\nfrom forge.data.rewards import MathReward, ThinkingReward\nimport re\n\n# Or define your own reward function\nclass CustomMathReward:\n    def __call__(self, prompt: str, response: str, target: str) -> float:\n        # Extract answer from response\n        match = re.search(r'\\\\boxed{([^}]+)}', response)\n        if not match:\n            return 0.0\n\n        answer = match.group(1).strip()\n        return 1.0 if answer == target else 0.0\n```\n\n### Step 3: Launch Training\n\n```bash\npython -m apps.grpo.main --config config\u002Fgrpo_math.yaml\n```\n\n### Step 4: Monitor Progress\n- [ ] Check W&B dashboard for loss curves\n- [ ] Verify entropy is decreasing (policy becoming more deterministic)\n- [ ] Monitor KL divergence (should stay bounded)\n\n---\n\n## Workflow 2: Custom Loss Function\n\nUse this workflow to implement new RL algorithms.\n\n### Step 1: Create Loss Class\n\n```python\n# src\u002Fforge\u002Flosses\u002Fcustom_loss.py\nimport torch\nimport torch.nn as nn\n\nclass CustomLoss(nn.Module):\n    def __init__(self, clip_range: float = 0.2, beta: float = 0.1):\n        super().__init__()\n        self.clip_range = clip_range\n        self.beta = beta\n\n    def forward(\n        self,\n        logprobs: torch.Tensor,\n        ref_logprobs: torch.Tensor,\n        advantages: torch.Tensor,\n        padding_mask: torch.Tensor,\n    ) -> torch.Tensor:\n        # Compute importance ratio\n        ratio = torch.exp(logprobs - ref_logprobs)\n\n        # Clipped policy gradient\n        clipped_ratio = torch.clamp(\n            ratio,\n            1 - self.clip_range,\n            1 + self.clip_range\n        )\n        pg_loss = -torch.min(ratio * advantages, clipped_ratio * advantages)\n\n        # KL penalty\n        kl = ref_logprobs - logprobs\n\n        # Apply mask and aggregate\n        masked_loss = (pg_loss + self.beta * kl) * padding_mask\n        loss = masked_loss.sum() \u002F padding_mask.sum()\n\n        return loss\n```\n\n### Step 2: Integrate into Application\n\n```python\n# apps\u002Fcustom\u002Fmain.py\nfrom forge.losses.custom_loss import CustomLoss\n\nloss_fn = CustomLoss(clip_range=0.2, beta=0.1)\n\n# In training loop\nloss = loss_fn(\n    logprobs=logprobs,\n    ref_logprobs=ref_logprobs,\n    advantages=advantages,\n    padding_mask=padding_mask,\n)\n```\n\n---\n\n## Workflow 3: Multi-GPU Distributed Training\n\nUse this workflow for scaling to multiple GPUs or nodes.\n\n### Configuration for Distributed\n\n```yaml\n# config\u002Fdistributed.yaml\nmodel: \"meta-llama\u002FMeta-Llama-3.1-8B-Instruct\"\n\nparallelism:\n  tensor_parallel_degree: 2    # Split model across GPUs\n  pipeline_parallel_degree: 1\n  data_parallel_shard_degree: 2\n\nservices:\n  generator:\n    procs: 2                   # 2 processes for TP=2\n    num_replicas: 1\n    with_gpus: true\n  trainer:\n    procs: 2\n    num_replicas: 1\n    with_gpus: true\n```\n\n### Launch with SLURM\n\n```bash\n# Submit job\nsbatch --nodes=2 --gpus-per-node=8 run_grpo.sh\n```\n\n### Launch Locally (Multi-GPU)\n\n```bash\n# 8 GPU setup\npython -m apps.grpo.main \\\n    --config config\u002Fdistributed.yaml \\\n    --trainer.procs 4 \\\n    --generator.procs 4\n```\n\n---\n\n## Core API Reference\n\n### Training Batch Format\n\ntorchforge uses dictionary-based batches for training:\n\n```python\n# inputs: list of dicts with torch.Tensor values\ninputs = [{\"tokens\": torch.Tensor}]\n\n# targets: list of dicts with training signals\ntargets = [{\n    \"response\": torch.Tensor,\n    \"ref_logprobs\": torch.Tensor,\n    \"advantages\": torch.Tensor,\n    \"padding_mask\": torch.Tensor\n}]\n\n# train_step returns loss as float\nloss = trainer.train_step(inputs, targets)\n```\n\n### Completion\n\nGenerated output from vLLM:\n\n```python\n@dataclass\nclass Completion:\n    text: str              # Generated text\n    token_ids: list[int]   # Token IDs\n    logprobs: list[float]  # Log probabilities\n    metadata: dict         # Custom metadata\n```\n\n---\n\n## Built-in Loss Functions\n\n### Loss Functions\n\nLoss functions are in the `forge.losses` module:\n\n```python\nfrom forge.losses import SimpleGRPOLoss, ReinforceLoss\n\n# SimpleGRPOLoss for GRPO training\nloss_fn = SimpleGRPOLoss(beta=0.1)\n\n# Forward pass\nloss = loss_fn(\n    logprobs=logprobs,\n    ref_logprobs=ref_logprobs,\n    advantages=advantages,\n    padding_mask=padding_mask\n)\n```\n\n### ReinforceLoss\n\n```python\nfrom forge.losses.reinforce_loss import ReinforceLoss\n\n# With optional importance ratio clipping\nloss_fn = ReinforceLoss(clip_ratio=0.2)\n```\n\n---\n\n## Common Issues and Solutions\n\n### Issue: Not Enough GPUs\n\n**Symptoms**: \"Insufficient GPU resources\" error\n\n**Solutions**:\n```yaml\n# Reduce service requirements\nservices:\n  generator:\n    procs: 1\n    with_gpus: true\n  trainer:\n    procs: 1\n    with_gpus: true\n  # Remove ref_model (uses generator weights)\n```\n\nOr use CPU for reference model:\n```yaml\nref_model:\n  with_gpus: false\n```\n\n### Issue: OOM During Generation\n\n**Symptoms**: CUDA OOM in vLLM\n\n**Solutions**:\n```yaml\n# Reduce batch size\ngrpo:\n  n_samples: 4  # Reduce from 8\n\n# Or reduce sequence length\ntraining:\n  seq_len: 2048\n```\n\n### Issue: Slow Weight Sync\n\n**Symptoms**: Long pauses between training and generation\n\n**Solutions**:\n```bash\n# Enable RDMA (if available)\nexport TORCHSTORE_USE_RDMA=1\n\n# Or reduce sync frequency\ntraining:\n  sync_interval: 10  # Sync every 10 steps\n```\n\n### Issue: Policy Collapse\n\n**Symptoms**: Entropy drops to zero, reward stops improving\n\n**Solutions**:\n```yaml\n# Increase KL penalty\ngrpo:\n  beta: 0.2  # Increase from 0.1\n\n# Or add entropy bonus\ntraining:\n  entropy_coef: 0.01\n```\n\n---\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fmeta-pytorch.org\u002Ftorchforge\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fmeta-pytorch\u002Ftorchforge\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002FYsTYBh6PD9\n- **TorchTitan**: https:\u002F\u002Fgithub.com\u002Fpytorch\u002Ftorchtitan\n- **Monarch**: https:\u002F\u002Fgithub.com\u002Fmeta-pytorch\u002Fmonarch\n\n",{"name":5,"license":15,"metadata":48,"description":7},{"tags":49,"author":55,"version":56,"dependencies":57},[50,51,38,52,53,54,36],"Reinforcement Learning","PyTorch","SFT","Monarch","TorchTitan","Orchestra Research","1.0.0",[58,59,60,61],"torch>=2.9.0","torchtitan>=0.2.0","vllm","monarch","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-torchforge",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":73,"prompt":83,"zipAvailable":84},[74,77,80],{"tool":75,"command":76},"npx","npx skills add davila7\u002Fclaude-code-templates --skill post-training-torchforge",{"tool":78,"command":79},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill post-training-torchforge",{"tool":81,"command":82},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill post-training-torchforge","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-torchforge\u002FSKILL.md",true,[86,92,96],{"path":87,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":90,"sourceHtmlUrl":91},"README.md","REFERENCE","参考文档",8861,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fbackend-to-frontend-handoff-docs\u002FREADME.md",{"path":93,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":94,"sourceHtmlUrl":95},"references\u002Fapi-reference.md",8685,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-torchforge\u002Freferences\u002Fapi-reference.md",{"path":97,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":98,"sourceHtmlUrl":99},"references\u002Ftroubleshooting.md",6709,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-torchforge\u002Freferences\u002Ftroubleshooting.md",[101,130,157,190,225,249],{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":112,"publishedAt":43},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[107],{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[113,114,115,116,117,25,118,119,34,24,120,121,122,123,124,125,126,127,128,129],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":131,"name":132,"nameZh":6,"description":133,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":134,"categories":135,"keywords":137,"publishedAt":43},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. 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Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[136],{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},[138,139,140,141,142,143,144,145,146,147,148,24,149,150,151,152,153,154,155,156],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":172,"publishedAt":43},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[163,166,167],{"categoryNo":164,"categoryName":165,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},{"categoryNo":168,"categoryName":169,"taxonomyType":170,"taxonomyTypeDesc":171},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[173,174,175,176,177,178,179,25,180,181,182,183,184,185,186,34,187,188,24,189],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":191,"name":192,"nameZh":6,"description":193,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":194,"licenseFileUrl":195,"sourceHtmlUrl":196,"categories":197,"keywords":201,"publishedAt":43},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). 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Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[232,235],{"categoryNo":233,"categoryName":234,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":199,"categoryName":200,"taxonomyType":110,"taxonomyTypeDesc":111},[237,238,239,240,241,203,242,212,213,243,244,245,246,25,247,121,248],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":250,"name":251,"nameZh":6,"description":252,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":194,"licenseFileUrl":253,"sourceHtmlUrl":254,"categories":255,"keywords":263,"publishedAt":43},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[256,259,260],{"categoryNo":257,"categoryName":258,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},{"categoryNo":261,"categoryName":262,"taxonomyType":170,"taxonomyTypeDesc":171},"01JBSEEDCAT000000000000036","内容创作者",[264,265,266,267,268,269,270,271,205,272,215,149,273,274,275,276,277,185,278,24,27,279,280,281,282,127,283],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","analysis","claims","argument","assemble","recommendations"]