RESEARCHMonitorWATCHLIST
Risk-Conditioned Fine-Tuning of Large Language Models
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Researchers propose risk-conditioned RLHF to adjust LLM risk aversion dynamically at inference time using Conditional Value-at-Risk.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 23 September 2026
- Collected by OneBench
- 24 Sept 2026, 03:02 UK
- Original headline
- Risk-Conditioned Fine-Tuning of Large Language Models ↗
Stored source excerpt
arXiv:2609.08064v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed in settings where rare but severe harmful generations can have significant consequences. Existing…
Short excerpt from the collected text, not the full source. Use the source link to read it in context.
The factual summary is a OneBench synthesis, not a quotation or independent verification. Collection time is not publication time. Open the source for its full context; related reporting can share the same underlying announcement.