SteerRM: Debiasing Reward Models via Sparse Autoencoders
SteerRM: Debiasing Reward Models via Sparse Autoencoders explores SteerRM offers a novel training-free method to debias reward models using Sparse Autoencoders.. Commercial viability score: 3/10 in Alignment and Debiasing.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisBehavioral Steering in a 35B MoE Language Model via SAE-Decoded Probe Vectors: One Agency Axis, Not Five TraitsViability: 2.0
- Prior WorkReal-Time Aligned Reward Model beyond SemanticsViability: 3.0
- Higher ViabilityReward Models Inherit Value Biases from PretrainingViability: 4.0
- Higher ViabilityCDRRM: Contrast-Driven Rubric Generation for Reliable and Interpretable Reward ModelingViability: 8.0
- Higher ViabilitySEM: Sparse Embedding Modulation for Post-Hoc Debiasing of Vision-Language Models