# Unlocking Agentic RL Training for GPT-OSS: A Practical Retrospective

DevFeed: [Unlocking Agentic RL Training for GPT-OSS: A Practical Retrospective](<https://devfeed.tech/articles/unlocking-agentic-rl-training-for-gpt-oss-a-practical-retrospective-7015.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/LinkedIn/gpt-oss-agentic-rl>)

Author: Jason Zhu; Hejian Sang; Arup De; Rohit Jain; Yanning Chen

Published: 2026-01-27T01:53:15Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [gpt-oss](<https://devfeed.tech/topics/gpt-oss.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [agentic-coding](<https://devfeed.tech/topics/agentic-coding.md>), [Retool](<https://devfeed.tech/topics/retool.md>), [Frameworks](<https://devfeed.tech/topics/frameworks.md>), [qwen](<https://devfeed.tech/topics/qwen.md>), [coding](<https://devfeed.tech/topics/coding.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-coding](<https://devfeed.tech/tags/agentic-coding.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [applications](<https://devfeed.tech/tags/applications.md>), [blog](<https://devfeed.tech/tags/blog.md>), [code](<https://devfeed.tech/tags/code.md>), [coding](<https://devfeed.tech/tags/coding.md>), [company](<https://devfeed.tech/tags/company.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gpt-oss](<https://devfeed.tech/tags/gpt-oss.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [linkedin](<https://devfeed.tech/tags/linkedin.md>), [models](<https://devfeed.tech/tags/models.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [rl](<https://devfeed.tech/tags/rl.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

LinkedIn's retrospective describes experiments to train GPT-OSS models for agentic reinforcement learning. It covers tool interaction, Harmony chat-template support, rollout and tool parsing, ReTool coding tasks, attention-sink fixes, and benchmark results using GPT-OSS-20B, GPT-OSS-120B, and Qwen-2.5-32B.

## Source excerpt

LinkedIn is an AI-first company that's built agents to help professionals be more successful. In this setting, models must reason over incomplete information, interact with structured services, and adapt to evolving user intent across multiple steps rather than produce a single static response.