Show HN: Reward Is Not Reinforcement Until Admitted

Category: ai-ml

Tags: ai-safety, reinforcement-learning, reward-hacking, experimental-framework, python

Score: 5.8/10 (Innovation: 6, Technical: 6, Documentation: 7, Utility: 4)

This project provides an experimental framework for a governance-based reward selection system in reinforcement learning, where rewards must pass multiple checks to be considered valid. It includes synthetic coding tasks and real-code benchmarks, comparing governed selectors against raw reward maximization. The concept is interesting for AI safety and robust reward modeling, though it remains a niche proof-of-concept.

Target audience: ai researchers, ml engineers, safety researchers

Repository: https://github.com/nikitph/rewarder · Python

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