[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7303YQJ2WN8Y7HKPVB3P9DM":3,"skill-same:01M7303YQJ2WN8Y7HKPVB3P9DM":86},{"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":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":46,"commitSha":47,"skillDirectory":48,"forksCount":49,"repositoryHtmlUrl":50,"repositoryHomepageUrl":6,"ownerHtmlUrl":51,"ownerBlogUrl":6,"ownerDescription":52,"updatedAt":53,"install":54,"resources":67},"01M7303YQJ2WN8Y7HKPVB3P9DM","openrlhf-training",null,"Framework RLHF de alta performance com aceleração Ray+vLLM. Use para treinamento PPO, GRPO, RLOO, DPO de modelos grandes (7B-70B+). Construído em Ray, vLLM, ZeRO-3. 2× mais rápido que DeepSpeedChat com arquitetura distribuída e compartilhamento de recursos GPU.","artubss\u002FSKILLS-CLAUDE-CODE","artubss","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F205203215?v=4","01M72YSJPDWM2V6S5PB6XP6PM9","Arthur Bezerra ","01M72YSJPZKPKRBGXNG8K6QFA8",12,"MIT","https:\u002F\u002Fgithub.com\u002Fartubss\u002FSKILLS-CLAUDE-CODE\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Fai-research\u002Fpost-training-openrlhf\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: openrlhf-training\ndescription: Framework RLHF de alta performance com aceleração Ray+vLLM. Use para treinamento PPO, GRPO, RLOO, DPO de modelos grandes (7B-70B+). Construído em Ray, vLLM, ZeRO-3. 2× mais rápido que DeepSpeedChat com arquitetura distribuída e compartilhamento de recursos GPU.\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 - Treinamento RLHF de Alta Performance\n\n## Início rápido\n\nOpenRLHF é um framework RLHF baseado em Ray otimizado para treinamento distribuído com aceleração de inferência vLLM.\n\n**Instalação**:\n```bash\n# Inicie contêiner Docker\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# Desinstale conflitos\nsudo pip uninstall xgboost transformer_engine flash_attn pynvml -y\n\n# Instale OpenRLHF com vLLM\npip install openrlhf[vllm]\n```\n\n**Treinamento PPO** (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**Treinamento GRPO** (Group Normalized Policy Optimization):\n```bash\n# Mesmo comando que PPO, mas adicione:\n--advantage_estimator group_norm\n```\n\n## Fluxos de trabalho comuns\n\n### Fluxo de trabalho 1: Pipeline RLHF completo (SFT → Reward Model → PPO)\n\n**Etapa 1: Treine modelo de reward** (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**Etapa 2: Treinamento PPO**:\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### Fluxo de trabalho 2: Treinamento GRPO (sem modelo critic necessário)\n\nAlternativa eficiente em memória ao 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**Parâmetros-chave do GRPO**:\n- `--advantage_estimator group_norm` - Ativa GRPO\n- `--use_kl_loss` - Perda KL do paper GRPO\n- `--kl_estimator k3` - Função de perda (k2 ≈ k1)\n- `--no_advantage_std_norm` - Desabilita normalização de desvio padrão\n\n### Fluxo de trabalho 3: Treinamento DPO (otimização de preferência)\n\nAlternativa mais simples sem modelo de reward:\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## Quando usar vs alternativas\n\n**Use OpenRLHF quando**:\n- Treinar modelos grandes (7B-70B+) com RL\n- Precisa de aceleração de inferência vLLM\n- Quer arquitetura distribuída com Ray\n- Tem cluster GPU multi-nó\n- Precisa de PPO\u002FGRPO\u002FRLOO\u002FDPO em um único framework\n\n**Seleção de algoritmo**:\n- **PPO**: Controle máximo, melhor para rewards complexos\n- **GRPO**: Eficiente em memória, sem critic necessário\n- **RLOO**: PPO modificado com KL por token\n- **REINFORCE++**: Mais estável que GRPO, mais rápido que PPO\n- **DPO**: Mais simples, sem modelo de reward necessário\n\n**Use alternativas em vez disso**:\n- **TRL**: Treinamento em nó único, API mais simples\n- **veRL**: Framework da ByteDance para modelos 671B\n- **DeepSpeedChat**: Integrado ao ecossistema DeepSpeed\n\n## Problemas comuns\n\n**Problema: OOM em GPU com modelos grandes**\n\nDesabilite colocalização de modelos:\n```bash\n# Remova a flag --colocate_all_models\n# Aloque GPUs separadas para cada modelo\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**Problema: Índice GPU fora do intervalo no DeepSpeed**\n\nConfigure variável de ambiente:\n```bash\nexport RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES=1\n```\n\n**Problema: Instabilidade no treinamento**\n\nUse Hybrid Engine em vez de assíncrono:\n```bash\n--colocate_all_models \\\n--vllm_enable_sleep \\\n--deepspeed_enable_sleep\n```\n\nAjuste coeficiente KL:\n```bash\n--init_kl_coef 0.05  # Aumente de 0.01\n```\n\n**Problema: Geração lenta durante PPO**\n\nAtive aceleração vLLM:\n```bash\n--vllm_num_engines 4 \\\n--vllm_tensor_parallel_size 2 \\\n--vllm_gpu_memory_utilization 0.5\n```\n\n## Tópicos avançados\n\n**Compartilhamento de GPU do Hybrid Engine**: Veja [references\u002Fhybrid-engine.md](references\u002Fhybrid-engine.md) para sleep mode vLLM, sleep mode DeepSpeed e alocação ideal de nós.\n\n**Comparação de algoritmos**: Veja [references\u002Falgorithm-comparison.md](references\u002Falgorithm-comparison.md) para benchmarks e hiperparâmetros de PPO vs GRPO vs RLOO vs REINFORCE++.\n\n**Configuração multi-nó**: Veja [references\u002Fmulti-node-training.md](references\u002Fmulti-node-training.md) para configuração de cluster Ray e tolerância a falhas.\n\n**Funções de reward customizadas**: Veja [references\u002Fcustom-rewards.md](references\u002Fcustom-rewards.md) para ajuste fino reforçado e RLHF de agentes.\n\n## Requisitos de hardware\n\n- **GPU**: NVIDIA A100\u002FH100 recomendado\n- **VRAM**:\n  - Modelo 7B: 8× A100 40GB (Hybrid Engine)\n  - Modelo 70B: 48× A100 80GB (vLLM:Actor:Critic = 1:1:1)\n- **Multi-nó**: Cluster Ray com InfiniBand recomendado\n- **Docker**: Container PyTorch NVIDIA 25.02+\n\n**Performance**:\n- 2× mais rápido que DeepSpeedChat\n- Aceleração de inferência vLLM\n- Hybrid Engine minimiza tempo ocioso de GPU\n\n## Recursos\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: Suporte da comunidade","# OpenRLHF - Treinamento RLHF de Alta Performance\n\n## Início rápido\n\nOpenRLHF é um framework RLHF baseado em Ray otimizado para treinamento distribuído com aceleração de inferência vLLM.\n\n**Instalação**:\n```bash\n# Inicie contêiner Docker\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# Desinstale conflitos\nsudo pip uninstall xgboost transformer_engine flash_attn pynvml -y\n\n# Instale OpenRLHF com vLLM\npip install openrlhf[vllm]\n```\n\n**Treinamento PPO** (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**Treinamento GRPO** (Group Normalized Policy Optimization):\n```bash\n# Mesmo comando que PPO, mas adicione:\n--advantage_estimator group_norm\n```\n\n## Fluxos de trabalho comuns\n\n### Fluxo de trabalho 1: Pipeline RLHF completo (SFT → Reward Model → PPO)\n\n**Etapa 1: Treine modelo de reward** (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**Etapa 2: Treinamento PPO**:\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### Fluxo de trabalho 2: Treinamento GRPO (sem modelo critic necessário)\n\nAlternativa eficiente em memória ao 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**Parâmetros-chave do GRPO**:\n- `--advantage_estimator group_norm` - Ativa GRPO\n- `--use_kl_loss` - Perda KL do paper GRPO\n- `--kl_estimator k3` - Função de perda (k2 ≈ k1)\n- `--no_advantage_std_norm` - Desabilita normalização de desvio padrão\n\n### Fluxo de trabalho 3: Treinamento DPO (otimização de preferência)\n\nAlternativa mais simples sem modelo de reward:\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## Quando usar vs alternativas\n\n**Use OpenRLHF quando**:\n- Treinar modelos grandes (7B-70B+) com RL\n- Precisa de aceleração de inferência vLLM\n- Quer arquitetura distribuída com Ray\n- Tem cluster GPU multi-nó\n- Precisa de PPO\u002FGRPO\u002FRLOO\u002FDPO em um único framework\n\n**Seleção de algoritmo**:\n- **PPO**: Controle máximo, melhor para rewards complexos\n- **GRPO**: Eficiente em memória, sem critic necessário\n- **RLOO**: PPO modificado com KL por token\n- **REINFORCE++**: Mais estável que GRPO, mais rápido que PPO\n- **DPO**: Mais simples, sem modelo de reward necessário\n\n**Use alternativas em vez disso**:\n- **TRL**: Treinamento em nó único, API mais simples\n- **veRL**: Framework da ByteDance para modelos 671B\n- **DeepSpeedChat**: Integrado ao ecossistema DeepSpeed\n\n## Problemas comuns\n\n**Problema: OOM em GPU com modelos grandes**\n\nDesabilite colocalização de modelos:\n```bash\n# Remova a flag --colocate_all_models\n# Aloque GPUs separadas para cada modelo\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**Problema: Índice GPU fora do intervalo no DeepSpeed**\n\nConfigure variável de ambiente:\n```bash\nexport RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES=1\n```\n\n**Problema: Instabilidade no treinamento**\n\nUse Hybrid Engine em vez de assíncrono:\n```bash\n--colocate_all_models \\\n--vllm_enable_sleep \\\n--deepspeed_enable_sleep\n```\n\nAjuste coeficiente KL:\n```bash\n--init_kl_coef 0.05  # Aumente de 0.01\n```\n\n**Problema: Geração lenta durante PPO**\n\nAtive aceleração vLLM:\n```bash\n--vllm_num_engines 4 \\\n--vllm_tensor_parallel_size 2 \\\n--vllm_gpu_memory_utilization 0.5\n```\n\n## Tópicos avançados\n\n**Compartilhamento de GPU do Hybrid Engine**: Veja [references\u002Fhybrid-engine.md](references\u002Fhybrid-engine.md) para sleep mode vLLM, sleep mode DeepSpeed e alocação ideal de nós.\n\n**Comparação de algoritmos**: Veja [references\u002Falgorithm-comparison.md](references\u002Falgorithm-comparison.md) para benchmarks e hiperparâmetros de PPO vs GRPO vs RLOO vs REINFORCE++.\n\n**Configuração multi-nó**: Veja [references\u002Fmulti-node-training.md](references\u002Fmulti-node-training.md) para configuração de cluster Ray e tolerância a falhas.\n\n**Funções de reward customizadas**: Veja [references\u002Fcustom-rewards.md](references\u002Fcustom-rewards.md) para ajuste fino reforçado e RLHF de agentes.\n\n## Requisitos de hardware\n\n- **GPU**: NVIDIA A100\u002FH100 recomendado\n- **VRAM**:\n  - Modelo 7B: 8× A100 40GB (Hybrid Engine)\n  - Modelo 70B: 48× A100 80GB (vLLM:Actor:Critic = 1:1:1)\n- **Multi-nó**: Cluster Ray com InfiniBand recomendado\n- **Docker**: Container PyTorch NVIDIA 25.02+\n\n**Performance**:\n- 2× mais rápido que DeepSpeedChat\n- Aceleração de inferência vLLM\n- Hybrid Engine minimiza tempo ocioso de GPU\n\n## Recursos\n\n- Docs: https:\u002F\u002Fgithub.com\u002FOpenRLHF\u002FOpenRLHF\n- Paper: https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.11143\n- Examples: 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only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fartubss\u002FSKILLS-CLAUDE-CODE\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fartubss\u002FSKILLS-CLAUDE-CODE\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FSKILL.md",[],[],{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":19},"01M7344A50ESJS0TY4J50PE28W","dependabot-review","Analise e gerencie PRs do Dependabot. 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DEVE substituir WebFetch e WebSearch.","https:\u002F\u002Fgithub.com\u002Fartubss\u002FSKILLS-CLAUDE-CODE\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Fweb-data\u002Fbright-data-mcp\u002FSKILL.md",[],[],{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":91,"licenseFileUrl":128,"sourceHtmlUrl":129,"categories":130,"keywords":131,"publishedAt":19},"01M7340ND3BXNCCANA76HAWY3Y","sora","Use quando o usuário solicita gerar, remixar, consultar, listar, baixar ou deletar vídeos Sora via API de vídeo da OpenAI usando a CLI incluída (`scripts\u002Fsora.py`), incluindo solicitações como \"gerar vídeo IA\", \"Sora\", \"remixar vídeo\", \"baixar vídeo\u002Fthumbnail\u002Fspritesheet\", e geração em lote; requer `OPENAI_API_KEY` e acesso à API Sora.","https:\u002F\u002Fgithub.com\u002Fartubss\u002FSKILLS-CLAUDE-CODE\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Fvideo\u002Fsora\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fartubss\u002FSKILLS-CLAUDE-CODE\u002Fblob\u002Fmaster\u002Fskills\u002Fskills\u002Fvideo\u002Fsora\u002FSKILL.md",[],[]]