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创建、改进和评估自定义技能,把专业流程转化为可重复使用的 AI 能力。
llm-vla-and-services【数据与研究】Use TorchRL LLM post-training, VLA data/actions, services, rendering, and weight-update surfaces safely.
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Use this sub-skill when a task mentions TorchRL LLM post-training, RLHF, GRPO, SFT, chat environments, LLM inference wrappers, tool execution services, Vision-Language-Action data, action tokenizers, service registry/lifecycle, rendering, video outputs, or LLM/VLA weight synchronization.
ChatEnv, History, PromptData, reward data, LLMCollector,
TransformersWrapper, vLLMWrapper, SGLangWrapper, GRPO, SFT,
distillation, policy version tracking, or vLLM/SGLang weight sync, read
LLM and RLHF workflows.validate_vla_tensordict, VLA canonical TensorDict keys,
RobotDatasetMetadata, action chunking, UniformActionTokenizer,
VocabTailActionTokenizer, ActionScaling, TinyVLA, LeRobot, or OpenX,
read VLA data and actions and run
check_vla_schema.py for a CPU-safe schema
smoke test.Service, get_services, owner/client lifecycles, direct/process/Ray
placement, PythonExecutorService, PythonInterpreter, rlrender, render
checkpoint loading, or video output, read
services and rendering and run
smoke_services.py for import/lifecycle checks.This sub-skill owns TorchRL's LLM/VLA/service/render-specific surfaces and the safe checks bundled here. Route generic collector topology, replay buffers, and weight updaters that are not LLM-specific to the collectors-and-replay area. Route generic actor/critic/key wiring to modules-and-policies, and generic loss module or trainer workflows to objectives-and-training.
Optional GPU serving backends, downloaded model checkpoints, Ray clusters, robot datasets, and codec/display stacks are reference-only here unless the user explicitly provisions those dependencies and hardware.