Paper

Configurable Reward Model for Balanced Safety Alignment

Unreadunread

AI summary

arXiv:2605. 30487v1 Announce Type: new Abstract: Aligning large language models (LLMs) to heterogeneous and rapidly evolving safety requirements remains a critical challenge.

Reading status

Priority

About this paper

Authors: Zhengping Jiang, Mehran Khodabandeh, Akash Bharadwaj, Manik Bhandari, Mayur Srungarapu, Anqi Liu, Benjamin Van Durme, Li Chen
Released: 2026-06-01 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Configurable Reward Model for Balanced Safety Alignment overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Coupling evil personas with wrong answers fails to protect Qwen2.5-7B from EM-induced alignment collapse — and the apparent capability ordering across coupling conditions is mostly eval contamination (LOW confidence)". Matching terms: class, alignment, line, rate, compare, without, alone, language. Source: arxiv cs.CL (NLP).
arXiv: 2605.30487

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Configurable Reward Model for Balanced Safety Alignment. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.