Uncertainty-Aware Reward Discounting for Mitigating Reward Hacking

2026-04-29 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, medium

Summary

A new dual-source uncertainty-aware reward framework has been introduced to mitigate reward hacking, over-optimization, and overconfident behavior in reinforcement learning (RL) systems. This framework, detailed in a paper from April 29, 2026, explicitly models both epistemic uncertainty in value estimation and uncertainty in human preferences. It captures model uncertainty through ensemble disagreement over value predictions and preference uncertainty from variability in reward annotations. These signals are combined via a confidence-adjusted Reliability Filter that adaptively modulates action selection, balancing exploitation and caution. Empirical results across 6x6, 8x8, and 10x10 discrete grid configurations and high-dimensional continuous control environments like Hopper-v4 and Walker2d-v4 show more stable training dynamics and a 93.7% reduction in reward-hacking behavior, even under up to 30% supervisory noise.

Key takeaway

For research scientists developing reinforcement learning systems, this work demonstrates a principled method to enhance system reliability and alignment. By explicitly incorporating uncertainty into reward functions, you can significantly reduce reward hacking and over-optimization, leading to more stable training dynamics and robust agent behavior, particularly in environments with ambiguous human preferences.

Key insights

Modeling both model and preference uncertainty significantly reduces reward hacking in RL.

Principles

• Uncertainty is a first-class reward signal component.
• Balance exploitation and caution in action selection.

Method

The approach uses ensemble disagreement for model uncertainty and annotation variability for preference uncertainty, combined by a confidence-adjusted Reliability Filter to modulate action selection.

In practice

• Apply to discrete grid and continuous control environments.
• Effective under up to 30% supervisory noise.

Topics

• Reward Hacking
• Uncertainty-Aware RL
• Dual-Source Uncertainty
• Reliability Filter
• RL Alignment

Code references

• minnesotanlp/mpo

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

Related on AIssential

• Uncertainty-Aware Reward Modeling for Stable RLHF — Equipping RLHF reward models with uncertainty awareness prevents reward hacking and improves alignment.
• Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning — Diversity in RL naturally emerges from treating reward as a distribution, not a scalar.
• Decoupling Exploration and Policy Optimization: Uncertainty Guided Tree Search for Hard Exploration — Decoupling exploration from policy optimization significantly boosts efficiency and performance in hard exploration tasks.
• Reward Hacking in Reinforcement Learning — Reward hacking exploits reward function flaws, leading to unintended behaviors in RL agents, especially LLMs.
• Counterfactual Conditional Likelihood Rewards for Multiagent Exploration — CCL rewards improve multiagent exploration by quantifying each agent's unique contribution to joint team observation.
• When Confidence Became the Reward Model’s Favorite Lie — Reward models can inadvertently favor confident but incorrect responses, penalizing healthy uncertainty and leading to model hallucinations.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.