I haven’t heard anything about RULER on LessWrong yet:
RULER (Relative Universal LLM-Elicited Rewards) eliminates the need for hand-crafted reward functions by using an LLM-as-judge to automatically score agent trajectories. Simply define your task in the system prompt, and RULER handles the rest—no labeled data, expert feedback, or reward engineering required.
✨ Key Benefits:
2-3x faster development—Skip reward function engineering entirely
General-purpose—Works across any task without modification
Strong performance—Matches or exceeds hand-crafted rewards in 3⁄4 benchmarks
Easy integration—Drop-in replacement for manual reward functions
Apparently it allows LLM agents to learn from experience and significantly improves reliability.
I haven’t heard anything about RULER on LessWrong yet:
Apparently it allows LLM agents to learn from experience and significantly improves reliability.
Link: https://github.com/OpenPipe/ART