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min readarXiv:2603.04861v1
Causally Robust Reward Learning from Reason-Augmented Preference Feedback
Authors: Minjune Hwang, Yigit Korkmaz, Daniel Seita, Erdem Bıyık
Pending (κ=0.55)Intermediatespurious
RSCT Score Breakdown
Relevance (R)
0.38
Superfluous (S)
0.32
Noise (N)
0.31
TL;DR
Causally Robust Reward Learning from Reason-Augmented Preference Feedback...
Causally Robust Reward Learning from Reason-Augmented Preference Feedback
RSCT Certification: κ=0.550 (certified) | RSN: 0.38/0.32/0.31 | Topics: spurious
Overview
This paper addresses topics relevant to RSCT research, specifically in the areas of spurious.
Key RSCT Relevance:
- Topic similarity score: 37%
- RSCT whitepaper similarity: 32%
- Combined relevance: 34%
RSCT Quality Metrics
| Metric | Value | Interpretation | |--------|-------|----------------| | κ-gate | 0.550 | Certified | | R (Relevance) | 0.375 | Direct relevance to research goals | | S (Stability) | 0.319 | Supporting context and patterns | | N (Noise) | 0.306 | Irrelevant components | | Gate Reached | 4 | Certification depth |
Paper Details
- Authors: Minjune Hwang, Yigit Korkmaz, Daniel Seita, Erdem Bıyık
- Source: arxiv
- Primary Topic: spurious
- Difficulty: Intermediate
This review was auto-generated by the Swarm-It RSCT discovery pipeline.