Enhancing RL Safety with Counterfactual LLM Reasoning
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912030570577920 |
|---|---|
| author | Gross, Dennis Spieker, Helge |
| author_facet | Gross, Dennis Spieker, Helge |
| contents | Reinforcement learning (RL) policies may exhibit unsafe behavior and are hard to explain. We use counterfactual large language model reasoning to enhance RL policy safety post-training. We show that our approach improves and helps to explain the RL policy safety. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10188 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing RL Safety with Counterfactual LLM Reasoning Gross, Dennis Spieker, Helge Machine Learning Reinforcement learning (RL) policies may exhibit unsafe behavior and are hard to explain. We use counterfactual large language model reasoning to enhance RL policy safety post-training. We show that our approach improves and helps to explain the RL policy safety. |
| title | Enhancing RL Safety with Counterfactual LLM Reasoning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.10188 |