# Sanctuary AI releases TIGER method to accelerate robot reinforcement learning with imitation policies as dense rewards

- Source: Sanctuary AI
- Published: 2026-10-02T20:17:05.000Z
- RoboHot: https://robohot.news/en/items/ozw2hyutfdw1uq81dfhngo773
- Original: https://sanctuary.ai/news/tiger/

## Summary

Sanctuary AI proposes TIGER (Task-Space Imitation Guidance for Efficient Reinforcement Learning), which does not execute the imitation policy but instead maps its action chunks through the controller into end-effector reference trajectories, converting them into dense rewards for reinforcement learning.

## Why it matters

The original text provides the specific approach for converting imitation policies into dense rewards along with ablation data, allowing readers to judge whether it can be applied to their own sparse-reward manipulation training.