[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADWYWFHK3QG4VHVT8Z2GYN":3,"skill-same:01M6ADWYWFHK3QG4VHVT8Z2GYN":103},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":61,"commitSha":62,"skillDirectory":63,"forksCount":64,"repositoryHtmlUrl":65,"repositoryHomepageUrl":66,"ownerHtmlUrl":67,"ownerBlogUrl":68,"ownerDescription":69,"updatedAt":70,"install":71,"resources":84},"01M6ADWYWFHK3QG4VHVT8Z2GYN","post-training-openrlhf",null,"High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.","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-openrlhf\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"training","post","post training openrlhf","RLHF","for","orchestra","high-performance","use","Transformers","ZeRO-3","Large Models","vLLM","Ray","RLOO","PPO","performance","OpenRLHF","Post-Training","GRPO","DPO","ZeRO","Distributed Training","DeepSpeed","gpu","framework","distributed","architecture","2026-11-10 10:12:30",false,"---\nname: post-training-openrlhf\ndescription: High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Post-Training, OpenRLHF, RLHF, PPO, GRPO, RLOO, DPO, Ray, vLLM, Distributed Training, Large Models, ZeRO-3]\ndependencies: [openrlhf, ray, vllm, torch, transformers, deepspeed]\n---\n\n# OpenRLHF - High-Performance RLHF Training\n\n## Quick start\n\nOpenRLHF is a Ray-based RLHF framework optimized for distributed training with vLLM inference acceleration.\n\n**Installation**:\n```bash\n# Launch Docker container\ndocker run --runtime=nvidia -it --rm --shm-size=\"10g\" --cap-add=SYS_ADMIN \\\n  -v $PWD:\u002Fopenrlhf nvcr.io\u002Fnvidia\u002Fpytorch:25.02-py3 bash\n\n# Uninstall conflicts\nsudo pip uninstall xgboost transformer_engine flash_attn pynvml -y\n\n# Install OpenRLHF with vLLM\npip install openrlhf[vllm]\n```\n\n**PPO Training** (Hybrid Engine):\n```bash\nray start --head --node-ip-address 0.0.0.0 --num-gpus 8\n\nray job submit --address=\"http:\u002F\u002F127.0.0.1:8265\" \\\n  --runtime-env-json='{\"working_dir\": \"\u002Fopenrlhf\"}' \\\n  -- python3 -m openrlhf.cli.train_ppo_ray \\\n  --ref_num_nodes 1 --ref_num_gpus_per_node 8 \\\n  --reward_num_nodes 1 --reward_num_gpus_per_node 8 \\\n  --critic_num_nodes 1 --critic_num_gpus_per_node 8 \\\n  --actor_num_nodes 1 --actor_num_gpus_per_node 8 \\\n  --vllm_num_engines 4 --vllm_tensor_parallel_size 2 \\\n  --colocate_all_models \\\n  --vllm_gpu_memory_utilization 0.5 \\\n  --pretrain OpenRLHF\u002FLlama-3-8b-sft-mixture \\\n  --reward_pretrain OpenRLHF\u002FLlama-3-8b-rm-700k \\\n  --save_path .\u002Foutput\u002Fllama3-8b-rlhf \\\n  --micro_train_batch_size 8 --train_batch_size 128 \\\n  --micro_rollout_batch_size 16 --rollout_batch_size 1024 \\\n  --max_epochs 1 --prompt_max_len 1024 --generate_max_len 1024 \\\n  --zero_stage 3 --bf16 \\\n  --actor_learning_rate 5e-7 --critic_learning_rate 9e-6 \\\n  --init_kl_coef 0.01 --normalize_reward \\\n  --gradient_checkpointing --packing_samples \\\n  --vllm_enable_sleep --deepspeed_enable_sleep\n```\n\n**GRPO Training** (Group Normalized Policy Optimization):\n```bash\n# Same command as PPO, but add:\n--advantage_estimator group_norm\n```\n\n## Common workflows\n\n### Workflow 1: Full RLHF pipeline (SFT → Reward Model → PPO)\n\n**Step 1: Train reward model** (DPO):\n```bash\ndeepspeed --module openrlhf.cli.train_rm \\\n  --save_path .\u002Foutput\u002Fllama3-8b-rm \\\n  --save_steps -1 --logging_steps 1 \\\n  --eval_steps -1 --train_batch_size 256 \\\n  --micro_train_batch_size 1 --pretrain meta-llama\u002FMeta-Llama-3-8B \\\n  --bf16 --max_epochs 1 --max_len 8192 \\\n  --zero_stage 3 --learning_rate 9e-6 \\\n  --dataset OpenRLHF\u002Fpreference_dataset_mixture2_and_safe_pku \\\n  --apply_chat_template --chosen_key chosen \\\n  --rejected_key rejected --flash_attn --gradient_checkpointing\n```\n\n**Step 2: PPO training**:\n```bash\nray start --head --node-ip-address 0.0.0.0 --num-gpus 8\n\nray job submit --address=\"http:\u002F\u002F127.0.0.1:8265\" \\\n  -- python3 -m openrlhf.cli.train_ppo_ray \\\n  --ref_num_nodes 1 --ref_num_gpus_per_node 8 \\\n  --reward_num_nodes 1 --reward_num_gpus_per_node 8 \\\n  --critic_num_nodes 1 --critic_num_gpus_per_node 8 \\\n  --actor_num_nodes 1 --actor_num_gpus_per_node 8 \\\n  --vllm_num_engines 4 --vllm_tensor_parallel_size 2 \\\n  --colocate_all_models \\\n  --pretrain OpenRLHF\u002FLlama-3-8b-sft-mixture \\\n  --reward_pretrain .\u002Foutput\u002Fllama3-8b-rm \\\n  --save_path .\u002Foutput\u002Fllama3-8b-ppo \\\n  --micro_train_batch_size 8 --train_batch_size 128 \\\n  --micro_rollout_batch_size 16 --rollout_batch_size 1024 \\\n  --max_epochs 1 --prompt_max_len 1024 --generate_max_len 1024 \\\n  --zero_stage 3 --bf16 \\\n  --actor_learning_rate 5e-7 --critic_learning_rate 9e-6 \\\n  --init_kl_coef 0.01 --normalize_reward \\\n  --vllm_enable_sleep --deepspeed_enable_sleep\n```\n\n### Workflow 2: GRPO training (no critic model needed)\n\nMemory-efficient alternative to PPO:\n\n```bash\nray job submit --address=\"http:\u002F\u002F127.0.0.1:8265\" \\\n  -- python3 -m openrlhf.cli.train_ppo_ray \\\n  --advantage_estimator group_norm \\\n  --ref_num_nodes 1 --ref_num_gpus_per_node 8 \\\n  --reward_num_nodes 1 --reward_num_gpus_per_node 8 \\\n  --actor_num_nodes 1 --actor_num_gpus_per_node 8 \\\n  --vllm_num_engines 4 --vllm_tensor_parallel_size 2 \\\n  --colocate_all_models \\\n  --pretrain OpenRLHF\u002FLlama-3-8b-sft-mixture \\\n  --reward_pretrain OpenRLHF\u002FLlama-3-8b-rm-700k \\\n  --save_path .\u002Foutput\u002Fllama3-8b-grpo \\\n  --micro_train_batch_size 8 --train_batch_size 128 \\\n  --micro_rollout_batch_size 16 --rollout_batch_size 1024 \\\n  --max_epochs 1 --bf16 \\\n  --actor_learning_rate 5e-7 \\\n  --init_kl_coef 0.01 --use_kl_loss --kl_estimator k3 \\\n  --normalize_reward --no_advantage_std_norm\n```\n\n**Key GRPO parameters**:\n- `--advantage_estimator group_norm` - Enables GRPO\n- `--use_kl_loss` - KL loss from GRPO paper\n- `--kl_estimator k3` - Loss function (k2 ≈ k1)\n- `--no_advantage_std_norm` - Disables std normalization\n\n### Workflow 3: DPO training (preference optimization)\n\nSimpler alternative without reward model:\n\n```bash\ndeepspeed --module openrlhf.cli.train_dpo \\\n  --save_path .\u002Foutput\u002Fllama3-8b-dpo \\\n  --save_steps -1 --logging_steps 1 \\\n  --eval_steps -1 --train_batch_size 256 \\\n  --micro_train_batch_size 2 --pretrain meta-llama\u002FMeta-Llama-3-8B \\\n  --bf16 --max_epochs 1 --max_len 8192 \\\n  --zero_stage 3 --learning_rate 5e-7 --beta 0.1 \\\n  --dataset OpenRLHF\u002Fpreference_dataset_mixture2_and_safe_pku \\\n  --apply_chat_template --chosen_key chosen \\\n  --rejected_key rejected --flash_attn --gradient_checkpointing\n```\n\n## When to use vs alternatives\n\n**Use OpenRLHF when**:\n- Training large models (7B-70B+) with RL\n- Need vLLM inference acceleration\n- Want distributed architecture with Ray\n- Have multi-node GPU cluster\n- Need PPO\u002FGRPO\u002FRLOO\u002FDPO in one framework\n\n**Algorithm selection**:\n- **PPO**: Maximum control, best for complex rewards\n- **GRPO**: Memory-efficient, no critic needed\n- **RLOO**: Modified PPO with per-token KL\n- **REINFORCE++**: More stable than GRPO, faster than PPO\n- **DPO**: Simplest, no reward model needed\n\n**Use alternatives instead**:\n- **TRL**: Single-node training, simpler API\n- **veRL**: ByteDance's framework for 671B models\n- **DeepSpeedChat**: Integrated with DeepSpeed ecosystem\n\n## Common issues\n\n**Issue: GPU OOM with large models**\n\nDisable model colocation:\n```bash\n# Remove --colocate_all_models flag\n# Allocate separate GPUs for each model\n--actor_num_gpus_per_node 8 \\\n--critic_num_gpus_per_node 8 \\\n--reward_num_gpus_per_node 8 \\\n--ref_num_gpus_per_node 8\n```\n\n**Issue: DeepSpeed GPU index out of range**\n\nSet environment variable:\n```bash\nexport RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES=1\n```\n\n**Issue: Training instability**\n\nUse Hybrid Engine instead of async:\n```bash\n--colocate_all_models \\\n--vllm_enable_sleep \\\n--deepspeed_enable_sleep\n```\n\nAdjust KL coefficient:\n```bash\n--init_kl_coef 0.05  # Increase from 0.01\n```\n\n**Issue: Slow generation during PPO**\n\nEnable vLLM acceleration:\n```bash\n--vllm_num_engines 4 \\\n--vllm_tensor_parallel_size 2 \\\n--vllm_gpu_memory_utilization 0.5\n```\n\n## Advanced topics\n\n**Hybrid Engine GPU sharing**: See [references\u002Fhybrid-engine.md](references\u002Fhybrid-engine.md) for vLLM sleep mode, DeepSpeed sleep mode, and optimal node allocation.\n\n**Algorithm comparison**: See [references\u002Falgorithm-comparison.md](references\u002Falgorithm-comparison.md) for PPO vs GRPO vs RLOO vs REINFORCE++ benchmarks and hyperparameters.\n\n**Multi-node setup**: See [references\u002Fmulti-node-training.md](references\u002Fmulti-node-training.md) for Ray cluster configuration and fault tolerance.\n\n**Custom reward functions**: See [references\u002Fcustom-rewards.md](references\u002Fcustom-rewards.md) for reinforced fine-tuning and agent RLHF.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA A100\u002FH100 recommended\n- **VRAM**:\n  - 7B model: 8× A100 40GB (Hybrid Engine)\n  - 70B model: 48× A100 80GB (vLLM:Actor:Critic = 1:1:1)\n- **Multi-node**: Ray cluster with InfiniBand recommended\n- **Docker**: NVIDIA PyTorch container 25.02+\n\n**Performance**:\n- 2× faster than DeepSpeedChat\n- vLLM inference acceleration\n- Hybrid Engine minimizes GPU idle time\n\n## Resources\n\n- Docs: https:\u002F\u002Fgithub.com\u002FOpenRLHF\u002FOpenRLHF\n- Paper: https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.11143\n- Examples: https:\u002F\u002Fgithub.com\u002FOpenRLHF\u002FOpenRLHF\u002Ftree\u002Fmain\u002Fexamples\n- Discord: Community support\n\n\n\n","# OpenRLHF - High-Performance RLHF Training\n\n## Quick start\n\nOpenRLHF is a Ray-based RLHF framework optimized for distributed training with vLLM inference acceleration.\n\n**Installation**:\n```bash\n# Launch Docker container\ndocker run --runtime=nvidia -it --rm --shm-size=\"10g\" --cap-add=SYS_ADMIN \\\n  -v $PWD:\u002Fopenrlhf nvcr.io\u002Fnvidia\u002Fpytorch:25.02-py3 bash\n\n# Uninstall conflicts\nsudo pip uninstall xgboost transformer_engine flash_attn pynvml -y\n\n# Install OpenRLHF with vLLM\npip install openrlhf[vllm]\n```\n\n**PPO Training** (Hybrid Engine):\n```bash\nray start --head --node-ip-address 0.0.0.0 --num-gpus 8\n\nray job submit --address=\"http:\u002F\u002F127.0.0.1:8265\" \\\n  --runtime-env-json='{\"working_dir\": \"\u002Fopenrlhf\"}' \\\n  -- python3 -m openrlhf.cli.train_ppo_ray \\\n  --ref_num_nodes 1 --ref_num_gpus_per_node 8 \\\n  --reward_num_nodes 1 --reward_num_gpus_per_node 8 \\\n  --critic_num_nodes 1 --critic_num_gpus_per_node 8 \\\n  --actor_num_nodes 1 --actor_num_gpus_per_node 8 \\\n  --vllm_num_engines 4 --vllm_tensor_parallel_size 2 \\\n  --colocate_all_models \\\n  --vllm_gpu_memory_utilization 0.5 \\\n  --pretrain OpenRLHF\u002FLlama-3-8b-sft-mixture \\\n  --reward_pretrain OpenRLHF\u002FLlama-3-8b-rm-700k \\\n  --save_path .\u002Foutput\u002Fllama3-8b-rlhf \\\n  --micro_train_batch_size 8 --train_batch_size 128 \\\n  --micro_rollout_batch_size 16 --rollout_batch_size 1024 \\\n  --max_epochs 1 --prompt_max_len 1024 --generate_max_len 1024 \\\n  --zero_stage 3 --bf16 \\\n  --actor_learning_rate 5e-7 --critic_learning_rate 9e-6 \\\n  --init_kl_coef 0.01 --normalize_reward \\\n  --gradient_checkpointing --packing_samples \\\n  --vllm_enable_sleep --deepspeed_enable_sleep\n```\n\n**GRPO Training** (Group Normalized Policy Optimization):\n```bash\n# Same command as PPO, but add:\n--advantage_estimator group_norm\n```\n\n## Common workflows\n\n### Workflow 1: Full RLHF pipeline (SFT → Reward Model → PPO)\n\n**Step 1: Train reward model** (DPO):\n```bash\ndeepspeed --module openrlhf.cli.train_rm \\\n  --save_path .\u002Foutput\u002Fllama3-8b-rm \\\n  --save_steps -1 --logging_steps 1 \\\n  --eval_steps -1 --train_batch_size 256 \\\n  --micro_train_batch_size 1 --pretrain meta-llama\u002FMeta-Llama-3-8B \\\n  --bf16 --max_epochs 1 --max_len 8192 \\\n  --zero_stage 3 --learning_rate 9e-6 \\\n  --dataset OpenRLHF\u002Fpreference_dataset_mixture2_and_safe_pku \\\n  --apply_chat_template --chosen_key chosen \\\n  --rejected_key rejected --flash_attn --gradient_checkpointing\n```\n\n**Step 2: PPO training**:\n```bash\nray start --head --node-ip-address 0.0.0.0 --num-gpus 8\n\nray job submit --address=\"http:\u002F\u002F127.0.0.1:8265\" \\\n  -- python3 -m openrlhf.cli.train_ppo_ray \\\n  --ref_num_nodes 1 --ref_num_gpus_per_node 8 \\\n  --reward_num_nodes 1 --reward_num_gpus_per_node 8 \\\n  --critic_num_nodes 1 --critic_num_gpus_per_node 8 \\\n  --actor_num_nodes 1 --actor_num_gpus_per_node 8 \\\n  --vllm_num_engines 4 --vllm_tensor_parallel_size 2 \\\n  --colocate_all_models \\\n  --pretrain OpenRLHF\u002FLlama-3-8b-sft-mixture \\\n  --reward_pretrain .\u002Foutput\u002Fllama3-8b-rm \\\n  --save_path .\u002Foutput\u002Fllama3-8b-ppo \\\n  --micro_train_batch_size 8 --train_batch_size 128 \\\n  --micro_rollout_batch_size 16 --rollout_batch_size 1024 \\\n  --max_epochs 1 --prompt_max_len 1024 --generate_max_len 1024 \\\n  --zero_stage 3 --bf16 \\\n  --actor_learning_rate 5e-7 --critic_learning_rate 9e-6 \\\n  --init_kl_coef 0.01 --normalize_reward \\\n  --vllm_enable_sleep --deepspeed_enable_sleep\n```\n\n### Workflow 2: GRPO training (no critic model needed)\n\nMemory-efficient alternative to PPO:\n\n```bash\nray job submit --address=\"http:\u002F\u002F127.0.0.1:8265\" \\\n  -- python3 -m openrlhf.cli.train_ppo_ray \\\n  --advantage_estimator group_norm \\\n  --ref_num_nodes 1 --ref_num_gpus_per_node 8 \\\n  --reward_num_nodes 1 --reward_num_gpus_per_node 8 \\\n  --actor_num_nodes 1 --actor_num_gpus_per_node 8 \\\n  --vllm_num_engines 4 --vllm_tensor_parallel_size 2 \\\n  --colocate_all_models \\\n  --pretrain OpenRLHF\u002FLlama-3-8b-sft-mixture \\\n  --reward_pretrain OpenRLHF\u002FLlama-3-8b-rm-700k \\\n  --save_path .\u002Foutput\u002Fllama3-8b-grpo \\\n  --micro_train_batch_size 8 --train_batch_size 128 \\\n  --micro_rollout_batch_size 16 --rollout_batch_size 1024 \\\n  --max_epochs 1 --bf16 \\\n  --actor_learning_rate 5e-7 \\\n  --init_kl_coef 0.01 --use_kl_loss --kl_estimator k3 \\\n  --normalize_reward --no_advantage_std_norm\n```\n\n**Key GRPO parameters**:\n- `--advantage_estimator group_norm` - Enables GRPO\n- `--use_kl_loss` - KL loss from GRPO paper\n- `--kl_estimator k3` - Loss function (k2 ≈ k1)\n- `--no_advantage_std_norm` - Disables std normalization\n\n### Workflow 3: DPO training (preference optimization)\n\nSimpler alternative without reward model:\n\n```bash\ndeepspeed --module openrlhf.cli.train_dpo \\\n  --save_path .\u002Foutput\u002Fllama3-8b-dpo \\\n  --save_steps -1 --logging_steps 1 \\\n  --eval_steps -1 --train_batch_size 256 \\\n  --micro_train_batch_size 2 --pretrain meta-llama\u002FMeta-Llama-3-8B \\\n  --bf16 --max_epochs 1 --max_len 8192 \\\n  --zero_stage 3 --learning_rate 5e-7 --beta 0.1 \\\n  --dataset OpenRLHF\u002Fpreference_dataset_mixture2_and_safe_pku \\\n  --apply_chat_template --chosen_key chosen \\\n  --rejected_key rejected --flash_attn --gradient_checkpointing\n```\n\n## When to use vs alternatives\n\n**Use OpenRLHF when**:\n- Training large models (7B-70B+) with RL\n- Need vLLM inference acceleration\n- Want distributed architecture with Ray\n- Have multi-node GPU cluster\n- Need PPO\u002FGRPO\u002FRLOO\u002FDPO in one framework\n\n**Algorithm selection**:\n- **PPO**: Maximum control, best for complex rewards\n- **GRPO**: Memory-efficient, no critic needed\n- **RLOO**: Modified PPO with per-token KL\n- **REINFORCE++**: More stable than GRPO, faster than PPO\n- **DPO**: Simplest, no reward model needed\n\n**Use alternatives instead**:\n- **TRL**: Single-node training, simpler API\n- **veRL**: ByteDance's framework for 671B models\n- **DeepSpeedChat**: Integrated with DeepSpeed ecosystem\n\n## Common issues\n\n**Issue: GPU OOM with large models**\n\nDisable model colocation:\n```bash\n# Remove --colocate_all_models flag\n# Allocate separate GPUs for each model\n--actor_num_gpus_per_node 8 \\\n--critic_num_gpus_per_node 8 \\\n--reward_num_gpus_per_node 8 \\\n--ref_num_gpus_per_node 8\n```\n\n**Issue: DeepSpeed GPU index out of range**\n\nSet environment variable:\n```bash\nexport RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES=1\n```\n\n**Issue: Training instability**\n\nUse Hybrid Engine instead of async:\n```bash\n--colocate_all_models \\\n--vllm_enable_sleep \\\n--deepspeed_enable_sleep\n```\n\nAdjust KL coefficient:\n```bash\n--init_kl_coef 0.05  # Increase from 0.01\n```\n\n**Issue: Slow generation during PPO**\n\nEnable vLLM acceleration:\n```bash\n--vllm_num_engines 4 \\\n--vllm_tensor_parallel_size 2 \\\n--vllm_gpu_memory_utilization 0.5\n```\n\n## Advanced topics\n\n**Hybrid Engine GPU sharing**: See [references\u002Fhybrid-engine.md](references\u002Fhybrid-engine.md) for vLLM sleep mode, DeepSpeed sleep mode, and optimal node allocation.\n\n**Algorithm comparison**: See [references\u002Falgorithm-comparison.md](references\u002Falgorithm-comparison.md) for PPO vs GRPO vs RLOO vs REINFORCE++ benchmarks and hyperparameters.\n\n**Multi-node setup**: See [references\u002Fmulti-node-training.md](references\u002Fmulti-node-training.md) for Ray cluster configuration and fault tolerance.\n\n**Custom reward functions**: See [references\u002Fcustom-rewards.md](references\u002Fcustom-rewards.md) for reinforced fine-tuning and agent RLHF.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA A100\u002FH100 recommended\n- **VRAM**:\n  - 7B model: 8× A100 40GB (Hybrid Engine)\n  - 70B model: 48× A100 80GB (vLLM:Actor:Critic = 1:1:1)\n- **Multi-node**: Ray cluster with InfiniBand recommended\n- **Docker**: NVIDIA PyTorch container 25.02+\n\n**Performance**:\n- 2× faster than DeepSpeedChat\n- vLLM inference acceleration\n- Hybrid Engine minimizes GPU idle time\n\n## Resources\n\n- Docs: https:\u002F\u002Fgithub.com\u002FOpenRLHF\u002FOpenRLHF\n- Paper: https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.11143\n- Examples: https:\u002F\u002Fgithub.com\u002FOpenRLHF\u002FOpenRLHF\u002Ftree\u002Fmain\u002Fexamples\n- Discord: Community support\n\n\n\n",{"name":5,"tags":51,"author":52,"license":15,"version":53,"description":7,"dependencies":54},[36,35,22,33,37,32,38,31,30,40,29,28],"Orchestra Research","1.0.0",[55,56,57,58,59,60],"openrlhf","ray","vllm","torch","transformers","deepspeed","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-openrlhf",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":72,"prompt":82,"zipAvailable":83},[73,76,79],{"tool":74,"command":75},"npx","npx skills add davila7\u002Fclaude-code-templates --skill post-training-openrlhf",{"tool":77,"command":78},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill post-training-openrlhf",{"tool":80,"command":81},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill post-training-openrlhf","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-openrlhf\u002FSKILL.md",true,[85,91,95,99],{"path":86,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Falgorithm-comparison.md","REFERENCE","参考文档",9783,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-openrlhf\u002Freferences\u002Falgorithm-comparison.md",{"path":92,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fcustom-rewards.md",15865,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-openrlhf\u002Freferences\u002Fcustom-rewards.md",{"path":96,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Fhybrid-engine.md",7266,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-openrlhf\u002Freferences\u002Fhybrid-engine.md",{"path":100,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002Fmulti-node-training.md",11099,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fpost-training-openrlhf\u002Freferences\u002Fmulti-node-training.md",[104,134,161,194,229,253],{"skillNo":105,"name":106,"nameZh":6,"description":107,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":108,"categories":109,"keywords":115,"publishedAt":46},"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",[110],{"categoryNo":111,"categoryName":112,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[116,117,118,119,120,23,121,122,26,123,124,125,126,127,128,129,130,131,132,133],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":138,"categories":139,"keywords":141,"publishedAt":46},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. 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",[140],{"categoryNo":111,"categoryName":112,"taxonomyType":113,"taxonomyTypeDesc":114},[142,143,144,145,146,147,148,149,150,151,152,123,153,154,155,156,157,158,159,160],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":162,"name":163,"nameZh":6,"description":164,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":165,"categories":166,"keywords":176,"publishedAt":46},"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",[167,170,171],{"categoryNo":168,"categoryName":169,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":111,"categoryName":112,"taxonomyType":113,"taxonomyTypeDesc":114},{"categoryNo":172,"categoryName":173,"taxonomyType":174,"taxonomyTypeDesc":175},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[177,178,179,180,181,182,183,23,184,185,186,187,188,189,190,26,191,192,123,193],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":198,"licenseFileUrl":199,"sourceHtmlUrl":200,"categories":201,"keywords":205,"publishedAt":46},"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). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[202],{"categoryNo":203,"categoryName":204,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000009","前端开发",[206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,23,228],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":230,"name":231,"nameZh":6,"description":232,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":198,"licenseFileUrl":233,"sourceHtmlUrl":234,"categories":235,"keywords":240,"publishedAt":46},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. 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",[236,239],{"categoryNo":237,"categoryName":238,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":203,"categoryName":204,"taxonomyType":113,"taxonomyTypeDesc":114},[241,242,243,244,245,207,246,216,217,247,248,249,250,23,251,125,252],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":254,"name":255,"nameZh":6,"description":256,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":198,"licenseFileUrl":257,"sourceHtmlUrl":258,"categories":259,"keywords":267,"publishedAt":46},"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",[260,263,264],{"categoryNo":261,"categoryName":262,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":111,"categoryName":112,"taxonomyType":113,"taxonomyTypeDesc":114},{"categoryNo":265,"categoryName":266,"taxonomyType":174,"taxonomyTypeDesc":175},"01JBSEEDCAT000000000000036","内容创作者",[268,269,270,271,272,273,274,275,209,276,219,153,277,278,279,280,281,189,282,123,283,284,285,286,287,131,288],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]