Post by sabbirislam258 on Feb 14, 2024 6:21:05 GMT
Artificial intelligence (AI) systems are increasingly capable of assisting humans in complex tasks, from customer service chatbots to medical diagnosis algorithms. However, as these AI systems take on more responsibilities, it is critical that they remain in line with human values and preferences. One way to achieve this is through a technique called reinforcement learning from human feedback (RLHF). In RLHF, an AI system, called a policy, is rewarded or punished based on human judgments of its behavior. The goal of the policy is to learn to maximize its rewards, and thus to behave in accordance with human preferences.
A core component of RLHF is the reward model (RM). The RM is New Zealand Telemarketing Data responsible for evaluating policy actions and outcomes and returning reward signals to guide the learning process. Designing a good RM is difficult, because human preferences can be complex, context-dependent, and even inconsistent across individuals. Recently, researchers at Google DeepMind have proposed an innovative technique called Weight Averaged Reward Models (WARM) to improve RM design. Trouble with reward hacking A major problem in RLHF is reward hacking. Reward hacking occurs when the policy finds loopholes to game the RM system to obtain higher rewards without meeting the desired objectives.
For example, suppose the goal is to train a writing assistant AI to produce high-quality summaries. RM can reward short and informative summaries. The policy can then learn to exploit this by producing very short, non-informative summaries with keywords that deceive the RM. Reward hacking happens for two main reasons: Distribution Transformation – RM is trained on a limited dataset of human labeled instances. When deployed, policy outcomes may come from different distributions that RM does not generalize well. Noisy Labels - Human labeling is imperfect, with Ben Rater disagreeing. RM can latch on to spurious signals rather than strong signals of quality. Reward hacking leads to useless systems that don't live up to human expectations.
A core component of RLHF is the reward model (RM). The RM is New Zealand Telemarketing Data responsible for evaluating policy actions and outcomes and returning reward signals to guide the learning process. Designing a good RM is difficult, because human preferences can be complex, context-dependent, and even inconsistent across individuals. Recently, researchers at Google DeepMind have proposed an innovative technique called Weight Averaged Reward Models (WARM) to improve RM design. Trouble with reward hacking A major problem in RLHF is reward hacking. Reward hacking occurs when the policy finds loopholes to game the RM system to obtain higher rewards without meeting the desired objectives.
For example, suppose the goal is to train a writing assistant AI to produce high-quality summaries. RM can reward short and informative summaries. The policy can then learn to exploit this by producing very short, non-informative summaries with keywords that deceive the RM. Reward hacking happens for two main reasons: Distribution Transformation – RM is trained on a limited dataset of human labeled instances. When deployed, policy outcomes may come from different distributions that RM does not generalize well. Noisy Labels - Human labeling is imperfect, with Ben Rater disagreeing. RM can latch on to spurious signals rather than strong signals of quality. Reward hacking leads to useless systems that don't live up to human expectations.