Core Insights - The AI field continues to evolve rapidly, with significant advancements in reasoning models and algorithms such as RLVR and GRPO, marking 2025 as a pivotal year for large language models (LLMs) [1][4][19] - DeepSeek R1's introduction has shifted the focus from merely stacking parameters to enhancing reasoning capabilities, demonstrating that high-performance models can be developed at a fraction of previously estimated costs [9][10][12] - The importance of collaboration between humans and AI is emphasized, reflecting on the boundaries of this partnership and the evolving role of AI in various tasks [1][4][66] Group 1: Reasoning Models and Algorithms - The year 2025 has been characterized as a "year of reasoning," with RLVR and GRPO algorithms gaining prominence in the development of LLMs [5][19] - DeepSeek R1's release showcased that reasoning behavior can be developed through reinforcement learning, enhancing the accuracy of model outputs [6][19] - The estimated training cost for the DeepSeek R1 model is significantly lower than previous assumptions, around $5.576 million, indicating a shift in cost expectations for advanced model training [10][12] Group 2: Focus Areas in LLM Development - Key focus areas for LLM development have evolved over the years, with 2025 emphasizing RLVR and GRPO, following previous years' focus on RLHF and LoRA techniques [20][22][24] - The trend of "Benchmaxxing" has emerged, highlighting the overemphasis on benchmark scores rather than real-world applicability of LLMs [60][63] - The integration of tools in LLM training has improved performance, allowing models to access external information and reduce hallucination rates [54][56] Group 3: Architectural Trends - The architecture of LLMs is converging towards using mixture of experts (MoE) layers and efficient attention mechanisms, indicating a shift towards more scalable and efficient models [43][53] - Despite advancements, traditional transformer architectures remain prevalent, with ongoing improvements in efficiency and engineering adjustments [43][53] Group 4: Future Directions - Future developments are expected to focus on expanding RLVR applications beyond mathematics and coding, incorporating reasoning evaluation into training signals [25][27] - Continuous learning is anticipated to gain traction, addressing challenges such as catastrophic forgetting while enhancing model adaptability [31][32] - The need for domain-specific data is highlighted as a critical factor for LLMs to establish a foothold in various industries, with proprietary data being a significant concern for companies [85][88]

Core Insights - DeepSeek has released a new mathematical model, DeepSeekMath-V2, focusing on self-verifiable mathematical reasoning [1][7] - The model has achieved gold medal-level scores in IMO 2025 and CMO 2024, and scored 118/120 in Putnam 2024, surpassing the highest human score of 90 [2][43] - DeepSeekMath-V2 is the first open-source IMO gold medal model, raising competitive pressure on companies like Google and OpenAI [4][5] Model Performance - DeepSeekMath-V2 outperforms GPT-5-Thinking-High and Gemini 2.5-Pro across all CNML problem categories, including algebra, geometry, number theory, combinatorics, and inequalities [2][34] - The model's architecture includes 685 billion parameters, emphasizing strong proof verification capabilities [7] Training Methodology - The training process involves an iterative reinforcement learning loop that alternates between optimizing the proof verifier and the proof generator [9] - A large dataset of 17,500 proof-required math problems was collected from AoPS competitions to train the proof verifier [12] - The verifier is trained to identify issues in proofs and assign scores based on three levels of correctness [10] Meta-Verification Mechanism - A meta-verification mechanism was introduced to enhance the verifier's accuracy by assessing the validity of the identified issues [14] - The meta-verifier is trained using a dataset created from expert evaluations of the verifier's output [15] Proof Generation - The trained verifier serves as a reward model for the proof generator, which learns to self-review and correct its outputs [23] - The reward structure encourages accurate self-assessment and correction of errors in generated proofs [27] Automation and Efficiency - The collaboration between the verifier and generator leads to a fully automated data labeling process, replacing time-consuming manual annotations [29][35] - The automated process ensures high consistency with expert evaluations, significantly improving efficiency [35] Experimental Results - The model's average quality score for proof analysis improved from 0.85 to 0.96, demonstrating the effectiveness of the meta-verification mechanism [21] - The model's ability to generate correct proofs was validated through rigorous testing, showing superior performance across various mathematical problem categories [34][39]

Core Insights - The article discusses the introduction of GRPO-Guard, a solution designed to mitigate the over-optimization problem observed in GRPO within flow models, ensuring faster convergence while significantly reducing the risk of over-optimization [3][35]. Group 1: GRPO and Over-Optimization Issues - GRPO has shown significant improvements in image and video generation flow models, but it suffers from a systematic bias in the importance ratio clipping mechanism, leading to over-optimization where the model's performance degrades despite rising proxy rewards [2][14]. - The empirical analysis indicates that the mean of the importance ratio is consistently below 1, which fails to effectively constrain overly confident positive gradients, resulting in suboptimal model performance in real applications [2][14]. Group 2: Introduction of GRPO-Guard - GRPO-Guard introduces two key improvements: RatioNorm, which normalizes the importance ratio distribution to bring the mean closer to 1, and Cross-Step Gradient Balancing, which ensures uniform exploration across the noise schedule [19][21]. - These enhancements restore the effectiveness of the clipping mechanism and stabilize policy updates, thereby alleviating the over-optimization phenomenon [35]. Group 3: Experimental Results - Experiments conducted on various GRPO variants and diffusion backbone models demonstrate that GRPO-Guard significantly alleviates over-optimization while maintaining or even improving performance compared to baseline methods [26][35]. - The results show that in baseline methods, the gold score exhibits a noticeable downward trend, while GRPO-Guard effectively mitigates this decline, indicating improved model robustness [26][28]. Group 4: Future Directions - The article suggests that while GRPO-Guard addresses over-optimization, it does not completely eliminate the issue, as there remains a significant gap between proxy scores and gold scores [35]. - Future efforts should focus on developing more accurate reward models to further reduce reward hacking and enhance optimization outcomes, providing a more reliable technical foundation for GRPO's application in flow models and broader generative tasks [35].

Core Insights - The article discusses the development of efficient GRPO (Generalized Reinforcement Policy Optimization) and its implications for reinforcement learning, highlighting the challenges and breakthroughs encountered during the research process [1][2]. Group 1: Research Development - The initial focus was on improving the speed of GRPO, with an emphasis on sampling efficiency, which is a common challenge in reinforcement learning [2][3]. - The author experimented with tree-based sampling methods but found that they did not yield the expected improvements in efficiency [3]. - A second approach involved "speculative sampling," which aimed to exit upon obtaining a correct sample, but faced implementation challenges that hindered performance [3][4]. Group 2: Methodological Innovations - The third approach utilized historical data to estimate the correctness of prompts, leading to a more efficient sampling strategy based on Bayesian methods [4]. - Experiments showed that reducing the number of rollouts per prompt did not significantly impact performance, indicating robustness in the methodology [4][5]. - The exploration of contrastive learning principles led to insights about the relationship between DPO (Direct Policy Optimization) and GRPO, suggesting potential avenues for further research [5]. Group 3: Community and Collaboration - The article emphasizes the importance of community engagement in advancing research, highlighting the role of discussions and collaborations in refining ideas and methodologies [8][10]. - The establishment of a comprehensive community focused on large model technologies aims to facilitate knowledge sharing and collaboration across various domains, including academic research and practical applications [9][10].

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]

Core Insights - Reinforcement Learning (RL) has become an essential technology in the AI field, particularly in large language models (LLMs) [1] - The Unsloth team has released a comprehensive reinforcement learning tutorial that covers various concepts from RLHF to GRPO, making it accessible for beginners and advanced users alike [2][3] Group 1: Understanding Reinforcement Learning - The goal of reinforcement learning is to increase the likelihood of achieving "good" outcomes while reducing the chances of "bad" outcomes [8][10] - Key components of RL include the environment, agent, actions, and reward functions, which collectively define the learning process [9][14] - RLHF (Reinforcement Learning from Human Feedback) has gained popularity, particularly through OpenAI's implementation, which trains agents to generate outputs deemed useful by humans [16][19] Group 2: GRPO and Its Advantages - GRPO (Group Relative Policy Optimization) is a method developed to train reasoning models, differing from PPO (Proximal Policy Optimization) by removing the value model and utilizing custom reward functions [22][24] - GRPO estimates average rewards through sampling multiple outputs for a given question, which helps in optimizing the model's performance [27][28] - The approach allows for significant memory savings and can enhance various tasks beyond coding and mathematics, such as email automation and legal applications [30] Group 3: Training with Unsloth - Unsloth provides a detailed guide for training reasoning models using GRPO, requiring a minimum of 5GB VRAM for local training of models up to 1.5 billion parameters [44] - The training process involves generating multiple answer variants for each question, evaluating them with a reward function, and updating model weights accordingly [45][57] - Effective training requires a well-designed reward function and a sufficient amount of data, with recommendations for at least 500 lines for optimal results [49][50] Group 4: Reward Functions and Validators - Reward functions and validators play crucial roles in evaluating model outputs, with the former assigning scores based on correctness and quality, while the latter verifies the accuracy of the outputs [46][56] - Examples of reward functions include those that reward correct answers and penalize incorrect or overly verbose responses [61] - The design of reward functions is critical, as poorly constructed ones can inadvertently degrade model performance [57]