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SkillsMP has collected 11 skills from inclusionAI/AReno. Open a skill to review its source and details.
Showing 11 of 11 collected skills.
Autonomously research, implement, train, evaluate, visualize, and submit a genuinely new AReno RLVR mini-game whose mechanically generated samples are independent state-to-single-output tasks. Use when the user requests a complete new game demo with real…
Add or debug an AReno model family, including config conversion, module construction, checkpoint load/save, text or multimodal inference, training backward, tensor parallelism, CUDA graph decode, and checkpoint round trips. Use only when model-specific…
Add or modify an AReno algorithm, trainer, loss, advantage calculation, role model, or algorithm-specific configuration. Use for framework-level SFT, DPO, GSPO, GRPO, PPO, or new optimization method development. Do not use merely to run an existing algorithm.
Create or debug an AReno multi-turn agentic dataset, run_agent implementation, tool schemas, tool execution, reward, loss masks, interactive TUI game with OpenAI-compatible LLM inference, or agentic training example. Use for text or multimodal agentic…
Diagnose failed, hung, slow, OOM, NaN, illegal-memory-access, NCCL, compilation, rollout, or training runs in AReno. Use when runtime evidence must identify the first causal stage. Do not use for routine capacity planning without a failure.
Develop, optimize, debug, and validate an AReno CUDA, Triton, fused, attention, convolution, routing, or MoE operator. Use when changes touch areno/accel or a runtime kernel boundary and require forward, backward, dtype, layout, CUDA graph, or benchmark…
Measure and diagnose AReno rollout, prefill, decode, training, checkpoint, role-switch, communication, or Python scheduling performance. Use when throughput or step time is slow and evidence from metrics, py-spy, or Nsight is required. Do not optimize before…
Start, validate, debug, and stop an AReno OpenAI-compatible serving endpoint. Use for areno serve commands, API probes, streaming, cancellation, cache or CUDA graph issues, and supported image or tool-call requests. Do not use for training jobs.
Run, configure, retry, and validate AReno SFT, DPO, GSPO, GRPO, PPO, and agentic training. Use for training commands, dataset or reward setup, smoke validation, real-step execution, checkpoint saving, or failed training retries. Do not use for serving-only…
Fit an AReno training or rollout workload to available GPUs by tuning TP, mini-batch, batch, sample count, and rollout concurrency. Use for OOM prevention, memory headroom, smoke-infer, smoke-train, or tune-params requests. Do not change semantic token limits…
Compare an AReno branch, model, checkpoint, algorithm, scheduler, or kernel against a baseline. Use for regression validation, logprob or metric comparison, checkpoint round trips, and correctness gates. Keep performance reporting separate.