))
Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
بایگانی مجموعه ها ("فیدهای غیر فعال" status)
When?
This feed was archived on February 21, 2025 21:08 (
Why? فیدهای غیر فعال status. سرورهای ما، برای یک دوره پایدار، قادر به بازیابی یک فید پادکست معتبر نبوده اند.
What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.
Manage episode 429711880 series 3498845
محتوای ارائه شده توسط BlueDot Impact. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط BlueDot Impact یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
This paper explains Anthropic’s constitutional AI approach, which is largely an extension on RLHF but with AIs replacing human demonstrators and human evaluators.
Everything in this paper is relevant to this week's learning objectives, and we recommend you read it in its entirety. It summarises limitations with conventional RLHF, explains the constitutional AI approach, shows how it performs, and where future research might be directed.
If you are in a rush, focus on sections 1.2, 3.1, 3.4, 4.1, 6.1, 6.2.
A podcast by BlueDot Impact.
Learn more on the AI Safety Fundamentals website.
فصل ها
1. Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback (00:00:00)
2. Abstract (00:00:30)
3. 3 Open Problems and Limitations of RLHF (00:01:23)
4. 3.1 Challenges with Obtaining Human Feedback (00:03:17)
5. 3.1.1 Misaligned Humans: Evaluators may Pursue the Wrong Goals (00:03:38)
6. 3.1.2 Good Oversight is Difficult (00:06:51)
7. 3.1.3 Data Quality (00:11:08)
8. 3.1.4 Limitations of Feedback Types (00:12:59)
9. 3.2 Challenges with the Reward Model (00:17:03)
10. 3.2.1 Problem Misspecification (00:17:27)
11. 3.2.2 Reward Misgeneralization and Hacking (00:20:24)
12. 3.2.3 Evaluating Reward Models (00:22:30)
13. 3.3 Challenges with the Policy (00:23:49)
14. 3.3.1 Robust Reinforcement Learning is Difficul (00:24:13)
15. 3.3.2 Policy Misgeneralization (00:26:23)
16. 3.3.3 Distributional Challenges (00:27:35)
17. 3.4 Challenges with Jointly Training the Reward Model and Policy (00:29:54)
85 قسمت
اشتراک گذاری
