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Notes on scalability
8 notes
Scaling Behavior Foundation Model for Humanoid Robots
The research focuses on improving the performance of Behavior Foundation Models (BFMs) used for controlling humanoid robots. The authors propose a scaling recipe that involves coordinating three core components: 1)…
Scaling Behavior Foundation Model for Humanoid Robots
Researchers have developed a new method for improving the performance of Behavior Foundation Models (BFMs) in controlling humanoid robots. The method involves coordinating three core components: motion tracking…
LLM-as-a-Verifier: A General-Purpose Verification Framework
The paper introduces LLM-as-a-Verifier, a general-purpose verification framework that provides fine-grained feedback to improve the accuracy of agentic tasks. The framework scales across multiple dimensions for better…
Steerability via constraints: a substrate for scalable oversight of coding agents
The author proposes a method for managing coding agents that mimics traditional methods used in large engineering teams, such as access control and strict coding conventions enforced by tools. They present a controlled…
Scaling Laws for Task-Specific LLM Distillation
This research provides empirical scaling laws for compressing Large Language Models (LLMs) specifically for certain domains, focusing on quantitative finance as the application domain. The study investigates the impact…
SPIRAL: Learning to Search and Aggregate
Researchers introduced SPIRAL, a framework for improving language model reasoning at test time. The framework uses three primitives - sequential reasoning within a trace, independently sampled parallel traces, and…
VeriEvol: Scaling Multimodal Mathematical Reasoning via Verifiable Evol-Instruct
The paper introduces VeriEvol, a framework that addresses scaling issues in reinforcement learning for visual mathematical reasoning. The framework decouples the difficulty of prompts and the reliability of answers…
VeriEvol: Scaling Multimodal Mathematical Reasoning via Verifiable Evol-Instruct
The paper presents VeriEvol, a framework for scaling reinforcement learning in visual mathematical reasoning. It addresses the challenge of maintaining reliable reward labels as data volume grows by treating scaling as…
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