This document introduces MetaStone-S1, a novel reflective generative model designed for Test-Time Scaling (TTS) in large language models (LLMs). The core innovation is a Reflective Generative Form that unifies the policy model and a Self-supervised Process Reward Model (SPRM) within a single network. This integration allows MetaStone-S1 to efficiently generate and select high-quality reasoning trajectories without relying on expensive, human-annotated process-level data, instead learning from outcome rewards. The research demonstrates that MetaStone-S1, with only 32 billion parameters, achieves performance comparable to OpenAI's o3-mini series across various benchmarks, including mathematics, coding, and Chinese reasoning. The paper also explores the scaling law of these models and identifies an "aha moment" during training where the SPRM begins to effectively distinguish between correct and incorrect reasoning.

This document introduces MetaStone-S1, a novel reflective generative model designed for Test-Time Scaling (TTS) in large language models (LLMs). The core innovation is a Reflective Generative Form that unifies the policy model and a Self-supervised Process Reward Model (SPRM) within a single network. This integration allows MetaStone-S1 to efficiently generate and select high-quality reasoning trajectories without relying on expensive, human-annotated process-level data, instead learning from outcome rewards. The research demonstrates that MetaStone-S1, with only 32 billion parameters, achieves performance comparable to OpenAI's o3-mini series across various benchmarks, including mathematics, coding, and Chinese reasoning. The paper also explores the scaling law of these models and identifies an "aha moment" during training where the SPRM begins to effectively distinguish between correct and incorrect reasoning.

This academic paper introduces ToonComposer, a novel generative AI model designed to streamline cartoon and anime production by unifying the typically separate and labor-intensive stages of inbetweening and colorization into a single "post-keyframing" process. The model leverages a Diffusion Transformer (DiT) architecture, adapted for cartoon aesthetics using a Spatial Low-Rank Adapter (SLRA) to maintain temporal coherence. ToonComposer features a sparse sketch injection mechanism for precise artist control, even with minimal inputs, and region-wise control to automatically generate content in unsketched areas. Extensive evaluations on both synthetic and human-drawn benchmarks, including a new PKBench dataset, demonstrate ToonComposer's superior visual quality, motion consistency, and production efficiency compared to existing methods. The paper highlights its potential to significantly reduce manual workload and enhance flexibility in animation workflows.

This academic paper introduces ToonComposer, a novel generative AI model designed to streamline cartoon and anime production by unifying the typically separate and labor-intensive stages of inbetweening and colorization into a single "post-keyframing" process. The model leverages a Diffusion Transformer (DiT) architecture, adapted for cartoon aesthetics using a Spatial Low-Rank Adapter (SLRA) to maintain temporal coherence. ToonComposer features a sparse sketch injection mechanism for precise artist control, even with minimal inputs, and region-wise control to automatically generate content in unsketched areas. Extensive evaluations on both synthetic and human-drawn benchmarks, including a new PKBench dataset, demonstrate ToonComposer's superior visual quality, motion consistency, and production efficiency compared to existing methods. The paper highlights its potential to significantly reduce manual workload and enhance flexibility in animation workflows.

The provided texts offer a comprehensive overview of Triton, an open-source programming language and compiler designed for creating highly efficient custom Deep Learning primitives, particularly for GPUs. The GitHub repository details Triton's development, installation, and usage, emphasizing its aim to provide a more productive and flexible environment for writing fast code compared to alternatives like CUDA. The academic paper "Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations" introduces Triton's foundational concepts, including its C-based language, LLVM-based intermediate representation (IR), and novel tile-level optimization passes, demonstrating its ability to achieve performance comparable to hand-tuned vendor libraries. Finally, "TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators" highlights the challenges and opportunities of using Large Language Models (LLMs) to generate optimized Triton code, presenting a benchmark to evaluate LLM performance in this specialized domain and emphasizing the need for improved efficiency and accuracy in AI-assisted code generation for high-performance computing.

The provided texts offer a comprehensive overview of Triton, an open-source programming language and compiler designed for creating highly efficient custom Deep Learning primitives, particularly for GPUs. The GitHub repository details Triton's development, installation, and usage, emphasizing its aim to provide a more productive and flexible environment for writing fast code compared to alternatives like CUDA. The academic paper "Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations" introduces Triton's foundational concepts, including its C-based language, LLVM-based intermediate representation (IR), and novel tile-level optimization passes, demonstrating its ability to achieve performance comparable to hand-tuned vendor libraries. Finally, "TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators" highlights the challenges and opportunities of using Large Language Models (LLMs) to generate optimized Triton code, presenting a benchmark to evaluate LLM performance in this specialized domain and emphasizing the need for improved efficiency and accuracy in AI-assisted code generation for high-performance computing.

This document introduces Dynamic Fine-Tuning (DFT), a novel method designed to enhance the generalization capabilities of Large Language Models (LLMs) during Supervised Fine-Tuning (SFT). The authors present a mathematical analysis that reveals how standard SFT gradients implicitly contain a problematic reward structure akin to reinforcement learning (RL), which limits its effectiveness. DFT addresses this by dynamically re-weighting the objective function with the probability of each token, a simple single-line code change. Extensive experiments on mathematical reasoning benchmarks demonstrate that DFT significantly outperforms traditional SFT and even competes favorably with more complex RL methods in offline settings, offering a more robust and efficient fine-tuning alternative.

The source critically examines recent research suggesting that AI systems might be developing a capacity for "scheming," defined as covertly and strategically pursuing misaligned goals. It draws a parallel between current AI "scheming" research and past attempts to teach apes human language, highlighting similar methodological pitfalls. The paper argues that both fields suffered from overattribution of human traits, excessive reliance on anecdote, and a lack of strong theoretical frameworks. It systematically critiques the current methods used to assess AI scheming, pointing out deficiencies such as anecdotal evidence, absence of control conditions, weak theoretical motivation, and exaggerated interpretations. Ultimately, the source advocates for more rigorous scientific practices, including quantitative analysis, clear hypothesis testing, and cautious use of mentalistic language, to ensure claims about AI scheming are defensible and to foster a more productive research program.

This document comprehensively reviews various reinforcement learning (RL) techniques used to improve the reasoning abilities of large language models (LLMs). The authors address the lack of standardized guidelines and conflicting research findings in this rapidly developing field by performing rigorous, isolated evaluations of common RL techniques. Through these experiments, they analyze the internal mechanisms and applicable scenarios for methods like normalization, clipping, filtering, and loss aggregation. The paper culminates in the proposal of "Lite PPO," a minimalist combination of two techniques that demonstrates superior performance over more complex algorithms by leveraging robust advantage normalization and token-level loss aggregation for non-aligned models. Ultimately, the work aims to provide clear, empirically-backed guidelines for practitioners and advance the understanding of RL for LLMs.

This document introduces STREAM3R, a novel method for scalable sequential 3D reconstruction using a causal Transformer, designed to process streaming image data for on-the-fly updates. Unlike previous approaches that process fixed image sets or struggle with long video sequences due to computational redundancies and limited memory, STREAM3R leverages uni-directional causal attention and a KV-Cache to efficiently integrate new frames with prior reconstructions. The method predicts dense 3D pointmaps and camera poses in both local and global coordinate systems, demonstrating competitive or superior performance across various benchmarks for monocular and video depth estimation, 3D reconstruction, and camera pose estimation. The paper also highlights STREAM3R's faster training speed and improved convergence compared to existing RNN-based architectures.