✳flâneur — a map of the web's best reading
Introducing OpenReward | General Reasoning
gr.inc · 959 words · saved by 1 readers
General Reasoning is a long horizon capabilities company.
Introducing OpenReward | General Reasoning Release March 2026 Introducing OpenReward An open resource for serving RL environments Reinforcement learning (RL) is a data-hungry paradigm. Unlike pre-training the bulk of data is not available on the internet. Instead, data is generated from environments sold by private data vendors. Because environments are excludable, and vendor contracts increasingly exclusive, the status quo favours incumbents and the closed ecosystem. Open models are therefore struggling to stay competitive in the reinforcement learning era. As part of our commitment to open r
Explore this link on the map →saved by
related reading
- A Taxonomy of RL Environments for LLM Agentsleehanchung.github.io
- Reward Hacking in Reinforcement Learning | Lil'Loglilianweng.github.io
- RL Environments and RL for Science: Data Foundries and Multi-Agent Architecturesnewsletter.semianalysis.com
- DeepSeek-R1arxiv.org
- RLHF: Reinforcement Learning from Human Feedbackhuyenchip.com
- Composer2.pdfcursor.com
- State of RL for reasoning LLMs | A. Weersaweers.de
- Pavlov's List: A List of RL Environment Startupspavlovslist.com
- Scaling Reinforcement Learning: Environments, Reward Hacking, Agents, Scaling Datasemianalysis.com
- Akash Bajwa on X: "RL Environments with Scale AI" / Xx.com
- Adithya S K on X: "RL Coding Environments 101: Why Harbor Exists" / Xx.com
- Don't Build an RL Environment Startupbenanderson.work