World PulseNow
Newsletter

Meta’s SPICE framework lets AI systems teach themselves to reason

VentureBeat — AI•Tuesday, November 11, 2025 at 10:21:00 PM
PositiveArtificial Intelligence
Meta’s SPICE framework lets AI systems teach themselves to reason
The development of the SPICE framework by researchers at Meta FAIR and the National University of Singapore marks a significant advancement in the field of artificial intelligence. This framework utilizes a self-play mechanism where two AI agents compete against each other, fostering an environment for self-improvement without human intervention. The goal of self-improving AI is to create systems that can dynamically adapt to their surroundings, enhancing their capabilities through interaction. Traditional reinforcement learning methods often rely on human-curated problem sets, which can limit their effectiveness. In contrast, SPICE aims to overcome these limitations by allowing AI agents to generate their own challenges, potentially leading to more robust AI systems capable of handling the unpredictability of real-world applications. As a proof-of-concept, SPICE could pave the way for future AI developments that prioritize adaptability and resilience.
— via World Pulse Now AI Editorial System
Read Original

Was this article worth reading? Share it

Recommended Readings
EvoLM: In Search of Lost Language Model Training Dynamics
arXiv — cs.LG4 hours ago
EvoLM: In Search of Lost Language Model Training Dynamics
PositiveArtificial Intelligence
EvoLM is a new model suite designed to analyze the training dynamics of language models (LMs) across various stages, including pre-training and fine-tuning. By training over 100 LMs with 1B and 4B parameters, EvoLM provides insights into the effectiveness of design choices and their impact on both language modeling and problem-solving capabilities. Key findings emphasize the diminishing returns of excessive pre-training and the importance of continued pre-training to mitigate forgetting during domain-specific tasks.
Read full article
via arXiv — cs.LG
Sector HQ Weekly Digest - November 17, 2025
DEV Community2 days ago
Sector HQ Weekly Digest - November 17, 2025
NeutralArtificial Intelligence
The Sector HQ Weekly Digest for November 17, 2025, highlights the latest developments in the AI industry, focusing on the performance of top companies. OpenAI leads with a score of 442385.7 and 343 events, followed by Anthropic and Amazon. The report also notes significant movements, with Sony jumping 277 positions in the rankings, reflecting the dynamic nature of the AI sector.
Read full article
via DEV Community
LDC: Learning to Generate Research Idea with Dynamic Control
arXiv — cs.CL2 days ago
LDC: Learning to Generate Research Idea with Dynamic Control
PositiveArtificial Intelligence
Recent advancements in large language models (LLMs) highlight their potential in automating scientific research ideation. Current methods often produce ideas that do not meet expert standards of novelty, feasibility, and effectiveness. To address these issues, a new framework is proposed that combines Supervised Fine-Tuning (SFT) and controllable Reinforcement Learning (RL) to enhance the quality of generated research ideas through a two-stage approach.
Read full article
via arXiv — cs.CL
Behaviour Policy Optimization: Provably Lower Variance Return Estimates for Off-Policy Reinforcement Learning
arXiv — cs.LG2 days ago
Behaviour Policy Optimization: Provably Lower Variance Return Estimates for Off-Policy Reinforcement Learning
PositiveArtificial Intelligence
The paper titled 'Behaviour Policy Optimization: Provably Lower Variance Return Estimates for Off-Policy Reinforcement Learning' addresses the challenges of high-variance return estimates in reinforcement learning algorithms. It highlights that well-designed behavior policies can collect off-policy data, leading to lower variance return estimates. This finding suggests that on-policy data collection is not optimal for variance, and the authors extend this insight to online reinforcement learning, where policy evaluation and improvement occur simultaneously.
Read full article
via arXiv — cs.LG
Mining--Gym: A Configurable RL Benchmarking Environment for Truck Dispatch Scheduling
arXiv — cs.LG2 days ago
Mining--Gym: A Configurable RL Benchmarking Environment for Truck Dispatch Scheduling
PositiveArtificial Intelligence
Mining-Gym is introduced as a configurable, open-source benchmarking environment aimed at optimizing truck dispatch scheduling in mining operations. The dynamic and stochastic nature of mining environments, characterized by uncertainties such as equipment failures and variable haul cycle times, poses challenges to traditional optimization methods. By leveraging Reinforcement Learning (RL), Mining-Gym provides a platform for training, testing, and evaluating RL algorithms, enhancing the efficiency and adaptability of decision-making in mining logistics.
Read full article
via arXiv — cs.LG
Thinker: Training LLMs in Hierarchical Thinking for Deep Search via Multi-Turn Interaction
arXiv — cs.CL2 days ago
Thinker: Training LLMs in Hierarchical Thinking for Deep Search via Multi-Turn Interaction
PositiveArtificial Intelligence
The article presents Thinker, a hierarchical thinking model designed to enhance the reasoning capabilities of large language models (LLMs) through multi-turn interactions. Unlike previous methods that relied on end-to-end reinforcement learning without supervision, Thinker allows for a more structured reasoning process by breaking down complex problems into manageable sub-problems. Each sub-problem is represented in both natural language and logical functions, improving the coherence and rigor of the reasoning process.
Read full article
via arXiv — cs.CL
Building the Web for Agents: A Declarative Framework for Agent-Web Interaction
arXiv — cs.CL2 days ago
Building the Web for Agents: A Declarative Framework for Agent-Web Interaction
PositiveArtificial Intelligence
The article discusses the introduction of VOIX, a declarative framework designed to enhance the interaction between AI agents and web interfaces. This framework allows developers to define actions and states through simple HTML tags, promoting reliable and privacy-preserving capabilities for AI agents. A study involving 16 developers demonstrated that participants could quickly create diverse agent-enabled web applications, highlighting the framework's practicality and effectiveness.
Read full article
via arXiv — cs.CL
DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control
arXiv — cs.LG2 days ago
DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control
PositiveArtificial Intelligence
The paper titled 'DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control' discusses the introduction of a disturbance-augmented Markov decision process (DAMDP) to enhance reinforcement learning in robotic control. It addresses the challenges of sim2real transfer, where models trained in simulation often fail to perform effectively in real-world scenarios due to discrepancies in system dynamics. The proposed approach aims to improve the robustness and stabilization of control responses in robotic systems.
Read full article
via arXiv — cs.LG