Paper: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

DeepSeek R1 uses RLHF with GRPO (a PPO variant) to improve LLM reasoning. A rule-based reward system guides the LLM, optimizing its policy via policy gradient methods. Another RLHF approach uses a learned reward model, iterative fine-tuning, and knowledge distillation, but faces challenges like unexpected behavior from poorly designed rewards. This segment provides a foundational explanation of language models, describing their function of predicting the next likely token in a sequence, and introduces reinforcement learning as a method for optimizing an agent's behavior to maximize rewards within an environment. The explanation uses clear analogies to make complex concepts accessible. This segment uses a relatable analogy of a cat navigating a house to illustrate the core concepts of reinforcement learning. It explains the agent (cat), its policy (decision-making), the environment (house), actions (movements), states (locations), and rewards (food vs. unpleasant objects), making the abstract concepts of reinforcement learning more concrete and understandable. This segment draws a parallel between language models and reinforcement learning agents. It explains how a language model's token selection can be viewed as an action within a reinforcement learning framework, where the goal is to train the model to choose tokens that maximize a reward based on predefined criteria or human feedback. This segment explains the process of aligning language models to follow instructions and adhere to specific standards. It describes the role of reinforcement learning from human feedback, where human annotators provide preferences, which are then used to train a reward model that guides the language model's behavior. The segment introduces the concept of instruction fine-tuning. The speaker proposes a deeper dive into the technical details of the GRPO objective function. The segment sets the stage for a more detailed explanation of the underlying principles of gradient policy optimization, which is crucial for understanding the reinforcement learning process in DeepSeq-R1.This segment provides an intuitive explanation of policy gradient optimization using the analogy of a company. The company's parameters are analogous to the policy parameters in reinforcement learning. The goal is to optimize these parameters to maximize the company's profit (or the agent's reward). The explanation clarifies the concept of tuning parameters to achieve a desired outcome. This segment delves into the GRPO algorithm, a key component of the DeepSeek R1 paper. It explains the algorithm's objective of optimizing the language model's policy to maximize rewards based on a dataset of preferences. The explanation breaks down the complex mathematical formulation into more manageable steps, focusing on the core idea of weighting actions based on their associated rewards.This segment provides a detailed explanation of the GRPO algorithm, using concrete examples to illustrate the concepts of trajectories, log probabilities, advantage terms, and reward models. It clarifies how the algorithm works step-by-step, making the complex mathematical concepts more accessible and understandable. The segment also discusses the use of a rule-based reward model in DeepSeek R1.The segment details the objective function used in training a language model via reinforcement learning. It explains how log probabilities of generated answers are weighted by an advantage term, reflecting the quality of the answer compared to alternatives, and how the model is trained to maximize this objective, encouraging it to generate better responses and discourage poor ones, unlike simpler supervised fine-tuning.This section explains the importance of the KL divergence term in the objective function. It illustrates how, without this term, the model might engage in "reward hacking," prioritizing maximizing reward over generating useful and factual responses. The KL divergence acts as a constraint, preventing the model from drastically altering its behavior while still improving its performance based on the reward signal.The speaker discusses the clipping mechanism used to prevent the model from becoming overly confident in its changes. It explains how clipping the ratio between log probabilities at different iterations prevents large, potentially destabilizing updates. The segment also reiterates the iterative optimization process: the model is refined by rewarding good outputs and penalizing bad ones, guided by a reward model.This segment describes the training of the reward model. It explains how annotator preferences are converted into numerical rewards using a model with a similar architecture to the policy model but with a different output head. The training process uses a pairwise comparison approach, aiming to assign higher rewards to preferred answers and lower rewards to less preferred ones.The speaker contrasts the reward model used in DeepSeq-R1 with the approach used in previous methods. Instead of a learned reward model, DeepSeq-R1 employs a rule-based system. This system assigns rewards based on whether the generated code compiles and runs within a time limit (for code generation tasks) or based on the correctness of the answer (for math problems). The system also rewards adherence to specific output formatting.This section presents results from DeepSeq-R1, highlighting the model's ability to generate longer responses through reinforcement learning. The model learns to generate longer responses because solving complex problems often requires a longer chain of thought. The segment emphasizes the importance of long-term rewards in reinforcement learning, where the reward signal is only received after a sequence of actions.The segment explains how the reward signal is propagated back to individual tokens through the advantage term. It compares the Generalized Proximal Policy Optimization (GRPO) algorithm used in DeepSeq-R1 with Proximal Policy Optimization (PPO). A key advantage of GRPO is that it doesn't require training a separate value function, simplifying the training process. This segment introduces the DeepSeek R1 paper and its approach to enhancing reasoning capabilities in language models. It highlights the use of reinforcement learning without supervised data, emphasizing the self-evolution aspect of the model's learning process, and introduces the GRPO algorithm used in the paper. The segment explains policy gradient optimization, a method where the gradient of an objective function (expected reward) guides parameter adjustments to improve the policy. It iteratively refines the policy by calculating the gradient with respect to parameters, indicating how to change them to increase the expected reward, thus enhancing the policy's performance. This segment discusses the limitations of policy gradient optimization, highlighting the intractability of exploring all possible trajectories. It explains the use of Monte Carlo estimation as an approximation, leading to high variance due to reliance on a limited sample of trajectories, and introduces the concept of baselines as a variance reduction technique. The segment provides a comprehensive explanation of the DeepSpeed-R1 loss function. It breaks down the components of the loss, including the ratio of probabilities from the current and previous policy iterations, and the advantage term, showing how these elements work together to guide policy updates while maintaining stability and preventing drastic changes.This segment focuses on the clipping mechanism and reward normalization within the DeepSpeed-R1 framework. It explains how clipping limits the influence of overly confident predictions, preventing drastic policy changes, while reward normalization ensures that the magnitude of rewards doesn't unduly influence the training process. The segment introduces the advantage term, a crucial element in reducing variance. It explains how this term, for each token, assesses the relative advantage of selecting that token compared to alternatives, guiding the language model towards more advantageous choices and improving the overall reward. This segment details off-policy learning, a technique to enhance training efficiency. Instead of repeatedly sampling trajectories after each policy update, off-policy learning samples a large set of trajectories initially and then iteratively optimizes the policy using these pre-sampled data, significantly reducing computational cost. This segment explains the knowledge distillation technique used to train smaller language models to mimic the behavior of larger models. It details how the smaller model learns not just the output of the larger model but also the probability distribution over possible outputs for each position in a sentence, leading to a more robust and accurate smaller model that avoids generating nonsensical outputs. The speaker emphasizes that this method provides a stronger signal for the smaller model to learn faster and more effectively than traditional training methods. The segment introduces knowledge distillation, a technique to transfer knowledge from a large, pre-trained model ("big brother") to a smaller model. It explains how the larger model's knowledge, including its probability distributions, guides the training of the smaller model, enabling it to learn more efficiently and effectively. This segment discusses potential side effects of training language models solely on reinforcement learning, such as the model mixing languages or exhibiting unexpected behaviors. The speaker argues that unless explicitly constrained, the model will explore all possible avenues to achieve the desired outcome. The segment highlights the importance of providing strong and clear signals through the reward system to guide the model's behavior and prevent undesirable side effects. The speaker emphasizes that with the right incentives, the model will find effective solutions. This segment focuses on reward generation in reinforcement learning, specifically distinguishing between outcome-based and process-based rewards. The speaker explains how outcome-based rewards, used in their previous work on PO, are distributed across previous tokens using an advantage term, allowing each token to carry information about its contribution to the final reward. The discussion then transitions to the challenges of process-based rewards and the limitations of applying them to complex problems. This segment delves into the challenges of using process reward models in reinforcement learning, highlighting the difficulty of dividing problems into sub-problems and assigning rewards to individual steps. The speaker contrasts this with Monte Carlo Tree Search, a technique that explores successful solution paths more extensively, but still yields inferior results compared to reinforcement learning approaches. The segment concludes by emphasizing the advantage of allowing the model to discover its own problem-solving strategies rather than explicitly defining them.