# reinforcement learning insights

Rangoon Insights reporting and guides tagged reinforcement learning.

Canonical: https://rangoon.ai/insights/tags/reinforcement-learning/

## Topics
- [Agent systems](https://rangoon.ai/insights/topics/agents/): Agent runtimes, handoffs, tools, and the boundaries that make them reviewable.
- [Governance](https://rangoon.ai/insights/topics/governance/): Policy, identity, authorization, approvals, and evidence for consequential work.
- [Infrastructure](https://rangoon.ai/insights/topics/infrastructure/): Model serving, sandboxes, tool services, and deployment boundaries.
- [Models](https://rangoon.ai/insights/topics/models/): Inference choices, local serving, compatibility, and operational tradeoffs.

## Stories
- [Physical Intelligence’s π*0.6 used deployment experience and corrections as VLA training inputs](https://rangoon.ai/insights/archive-physical-intelligence-pistar06-recap-november-2025/)
  Physical Intelligence introduced π*0.6 and its RECAP method on November 17, 2025, combining demonstrations, autonomous rollouts and expert corrections for robot-policy training.
- [QwQ-32B made reinforcement-learning claims a harness question](https://rangoon.ai/insights/archive-qwq-32b-release-2025/)
  Qwen released QwQ-32B on March 6, 2025 and described a 32B reasoning model with agent-facing capability claims. Its comparisons need the stated tasks, budgets, and scaffolding.
- [The DeepSeek-R1 paper made the training path part of the story](https://rangoon.ai/insights/archive-deepseek-r1-paper-submission-2025/)
  DeepSeek submitted its DeepSeek-R1 paper to arXiv on January 22, 2025. Its account of reinforcement learning and distillation called for claim-by-claim model evaluation.


## More information

- [Documentation index](https://rangoon.ai/llms.txt)
- [AI access and policies](https://rangoon.ai/ai/)
- [Source repository](https://github.com/hypler-dev/rangoon)