How-to11月13日 03:03

Anyscale 网络研讨会：强化学习后训练与 SkyRL 实战

Anyscale@anyscalecompute

Post

Why we picked it

Anyscale 要开一场 RL 后训练的实操研讨会，从 GRPO、OPD 蒸馏讲到 SkyRL 的异步训练和 rollout 调优，还会拆解怎么给终端 agent 建 eval 和自定义环境。想自己搭 RL 训练管线的开发者值得报名听听。

Anyscale 宣布下周举办一场关于强化学习后训练的网络研讨会，报名即将截止。议程涵盖底层算法原理，包括使用 GRPO 的 RLVR（可验证奖励的强化学习）。

Body

研讨会还将介绍近期研究进展，如 on-policy distillation（OPD）和 multi-teacher on-policy distillation（MOPD）。实战部分聚焦如何后训练一个终端 agent，涉及构建评测集、自定义环境与 RL 系统搭建。

最后会对 SkyRL 框架做深入讲解，包括异步 RL、性能监控以及 RL rollout 的调优方法。适合想在自己的模型上跑 RL 后训练流程的工程师。

Original · Anyscale

Last chance to sign up for next week's webinar!

Don't miss the opportunity to learn about reinforcement learning for post-training: - How the underlying algorithms work (e.g., RLVR with GRPO) - Recent research advances (on-policy distillation (OPD), multi-teacher on-policy distillation (MOPD)) - Details of post-training a terminal agent (building evals, building custom environments, RL systems) - SkyRL deep dive (async RL, performance monitoring, tuning for RL rollouts)

Industry Coordinates & Source Verification

This report is categorized under AITOP's "How-to" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include SkyRL, Anyscale, GRPO, 强化学习.

Source attribution: curated from Anyscale (@anyscalecompute).

#SkyRL #Anyscale #GRPO #强化学习 #提示词工程

Keep reading

Recent stories with the same tag or section

How-to BuildersSelected02:01

Anyscale session on LLM post-training, RLVR and SkyRL

The most useful part is the terminal-agent case study, which shows how evals, custom environments and RL infrastructure fit together in a real post-training pipeline rather than as isolated concepts. Engineers building agents or running reinforcement learning fine-tuning can use SkyRL and the async rollout workflow to structure their own training runs. The OPD and MOPD sections also outline distillation methods rarely covered in tutorials.

#SkyRL #Anyscale #RLVR #GRPO

Anyscale@anyscalecomputeSource

How-to BuildersSelected04:03

Anyscale Offers Hands-On Training on LLM Post-Training with SkyRL

Agentic RL with GRPO and on-policy distillation are moving from research papers into standard production toolkits, and this course shows how teams actually wire them up with SkyRL, evals and custom environments. Engineers building agent training pipelines can follow the case-study structure directly. Registration is via Anyscale's official link, so seats and schedule depend on their listed availability.

#Anyscale #SkyRL #GRPO #on-policy distillation

Event

Anyscale@anyscalecomputeSource

Models BuildersSelected23:29

Reflection-Beam-1.0 gets Day-0 support in vLLM

Day-0 serving and RL support means practitioners can deploy and fine-tune a 501B-parameter sparse model without waiting for ecosystem catch-up. Teams running large-scale inference can start with vLLM and add reinforcement learning through SkyRL's LoRA or MXFP8 paths. The 1M-token context window puts Beam in direct competition with other frontier sparse models for long-document and agentic workloads.

#Reflection-Beam-1.0 #vLLM #SkyRL #open-source models

Official accountvLLM@vllm_projectSource

Models BuildersSelected21:28

Reflection-Beam-1.0 Gains Day-0 Support in vLLM

The sparse-activation design at 501B/23B active with a 1M-token window makes Beam a candidate for long-context workloads without full MoE serving costs. Teams running vLLM can deploy it immediately and fine-tune with SkyRL's LoRA or MXFP8 RL paths. Compared with other large reasoning models, Beam's Day-0 coverage of both serving and RL tooling is the differentiator.

#Reflection #vLLM #SkyRL #open-source models

Official accountvLLM@vllm_projectSource

How-to BuildersOfficialSelected00:24

AWS shows SkyRL recipe for post-training Qwen3-VL-8B with GRPO

Multimodal RL post-training of open VLMs is becoming a reproducible recipe rather than a research one-off, with the trained LoRA adapter ready to deploy directly on SageMaker. Teams building vision-language applications can follow the steps to fine-tune Qwen3-VL-8B on their own data using GRPO without managing infrastructure manually. It also gives Qwen a concrete deployment path on AWS infrastructure.

#SkyRL #Qwen3-VL-8B #SageMaker HyperPod #GRPO

Primary sourceAWS Machine Learning Blog@Nilesh PSSource