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Gradient Hacking: Definitions and Examples
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Gradient hacking is a hypothesized phenomenon where:
- A model has knowledge about possible training trajectories which isn’t being used by its training algorithms when choosing updates (such as knowledge about non-local features of its loss landscape which aren’t taken into account by local optimization algorithms).
- The model uses that knowledge to influence its medium-term training trajectory, even if the effects wash out in the long term.
Below I give some potential examples of gradient hacking, divided into those which exploit RL credit assignment and those which exploit gradient descent itself. My concern is that models might use techniques like these either to influence which goals they develop, or to fool our interpretability techniques. Even if those effects don’t last in the long term, they might last until the model is smart enough to misbehave in other ways (e.g. specification gaming, or reward tampering), or until it’s deployed in the real world—especially in the RL examples, since convergence to a global optimum seems unrealistic (and ill-defined) for RL policies trained on real-world data. However, since gradient hacking isn’t very well-understood right now, both the definition above and the examples below should only be considered preliminary.
Source:
https://www.alignmentforum.org/posts/EeAgytDZbDjRznPMA/gradient-hacking-definitions-and-examples
Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.
---
A podcast by BlueDot Impact.
Learn more on the AI Safety Fundamentals website.
Kapitler
1. Gradient Hacking: Definitions and Examples (00:00:00)
2. RL credit hacking examples (00:01:26)
3. Gradient descent hacking examples (00:03:15)
85 episoder
Fetch error
Hmmm there seems to be a problem fetching this series right now. Last successful fetch was on January 02, 2025 12:05 (
What now? This series will be checked again in the next hour. If you believe it should be working, please verify the publisher's feed link below is valid and includes actual episode links. You can contact support to request the feed be immediately fetched.
Manage episode 424087970 series 3498845
Gradient hacking is a hypothesized phenomenon where:
- A model has knowledge about possible training trajectories which isn’t being used by its training algorithms when choosing updates (such as knowledge about non-local features of its loss landscape which aren’t taken into account by local optimization algorithms).
- The model uses that knowledge to influence its medium-term training trajectory, even if the effects wash out in the long term.
Below I give some potential examples of gradient hacking, divided into those which exploit RL credit assignment and those which exploit gradient descent itself. My concern is that models might use techniques like these either to influence which goals they develop, or to fool our interpretability techniques. Even if those effects don’t last in the long term, they might last until the model is smart enough to misbehave in other ways (e.g. specification gaming, or reward tampering), or until it’s deployed in the real world—especially in the RL examples, since convergence to a global optimum seems unrealistic (and ill-defined) for RL policies trained on real-world data. However, since gradient hacking isn’t very well-understood right now, both the definition above and the examples below should only be considered preliminary.
Source:
https://www.alignmentforum.org/posts/EeAgytDZbDjRznPMA/gradient-hacking-definitions-and-examples
Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.
---
A podcast by BlueDot Impact.
Learn more on the AI Safety Fundamentals website.
Kapitler
1. Gradient Hacking: Definitions and Examples (00:00:00)
2. RL credit hacking examples (00:01:26)
3. Gradient descent hacking examples (00:03:15)
85 episoder
Alle episoder
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1 Illustrating Reinforcement Learning from Human Feedback (RLHF) 22:32
1 Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback 32:19
1 Constitutional AI Harmlessness from AI Feedback 1:01:49
1 Empirical Findings Generalize Surprisingly Far 11:32
1 Two-Turn Debate Doesn’t Help Humans Answer Hard Reading Comprehension Questions 16:39
1 Least-To-Most Prompting Enables Complex Reasoning in Large Language Models 16:08
1 ABS: Scanning Neural Networks for Back-Doors by Artificial Brain Stimulation 16:08
1 Imitative Generalisation (AKA ‘Learning the Prior’) 18:14
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