NVIDIA's Polar Enables Agentic RL on Any Harness at Scale
SergioPaniego · x · 2026-08-20
The post discusses a new work, "Polar: Agentic RL on Any Harness at Scale," which turns existing agent harnesses (like Codex, Claude Code, Qwen Code) into RL training environments without modifying their internals. This approach is credited for the high performance of frontier agents. The post also links to related blog posts and a full worked example using GRPO.
More from coding & agent
- Decision models match LLMs on routing and safety — and are 7-8x faster — randal_olson · 2026-10-16
- Temporal Serverless Workers on Cloud Run: queue-driven autoscaling, scale to zero — rseroter · 2026-10-16
- Meta's Superintelligence Lab Runs 200+ Direct Reports—A Lesson for Flat Multi-Agent Systems — peterjliu · 2026-10-16
- LangChain CEO: the most interesting agent harness work lives at the harness-infra boundary — hwchase17 · 2026-10-16
- GitSwarm paper: agents share branchable Git memory, solving all 30 IMOProofBench problems in one run — anirudhg9119 · 2026-10-16
- Hands-on with Gemini 4 Argon: faster than Opus 5.5 but hallucinates more and costs more — prasenx · 2026-10-16