如何准备RL面试相关的问题？

Core Insights - The article discusses the GRPO (Group Relative Policy Optimization) framework, primarily categorizing it as on-policy but acknowledging its potential off-policy adaptations [5][6][7] - It emphasizes the importance of understanding the data sources and the implications of using old policy data in the context of on-policy and off-policy learning [10][11] GRPO Framework - GRPO is typically considered on-policy as it estimates group-relative advantage using data generated by the current behavior policy [5][6] - Recent works have explored off-policy adaptations of GRPO, utilizing data from older policies to enhance sample efficiency and stability [4][5][7] - The original implementation of GRPO relies on current policy data to estimate gradients and advantages, aligning with traditional on-policy definitions [6][10] Importance Sampling - Importance Sampling (IS) is a key method in off-policy evaluation, allowing the use of data from a behavior policy to assess the value of a target policy [8][9] - The article outlines the mathematical formulation of IS, highlighting its role in correcting biases arising from differences in sampling distributions [12][14] - Weighted Importance Sampling is introduced as a solution to the high variance problem associated with basic IS [15][16][17] GSPO and DAPO - GSPO (Group Sequence Policy Optimization) addresses high variance and instability issues in GRPO/PPO by shifting the focus to sequence-level importance ratios [18][21] - DAPO (Decoupled Clip & Dynamic Sampling Policy Optimization) enhances training stability and sample efficiency in long chain-of-thought tasks through various engineering techniques [20][24] - Both GSPO and DAPO aim to improve the robustness of training processes in large-scale language models, particularly in handling long sequences and mitigating entropy collapse [20][24][27] Entropy Collapse - Entropy collapse refers to the rapid decrease in policy randomness during training, leading to reduced exploration and potential suboptimal convergence [28][30] - The article discusses various strategies to mitigate entropy collapse, including entropy regularization, KL penalties, and dynamic sampling [32][33][34] - It emphasizes the need for a balance between exploration and exploitation to maintain effective training dynamics [37][41] Relationship Between Reward Hacking and Entropy Collapse - Reward hacking occurs when an agent finds shortcuts to maximize rewards, often leading to entropy collapse as the policy becomes overly deterministic [41][42] - The article outlines the cyclical relationship between reward hacking and entropy collapse, suggesting that addressing one can help mitigate the other [41][42] - Strategies for managing both issues include refining reward functions, enhancing training stability, and ensuring diverse sampling [47][48]