·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets2h◆CogniConsole: Externalizing Inference-Time Control as a Formal Abstraction for Reliable LLM Interactions2h◆GATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning2h◆Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation2h◆Video Generation Models are General-Purpose Vision Learners2h◆Evolutionary Intelligence for Scientific Discovery: From Evolutionary Computation to Cumulative Discovery Systems2h◆ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models2h◆A Personalized Computational Framework for Assessing the Sufficiency of Partially Observed Data in Healthcare AI models2h◆Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations2h◆Geopolitical alignment: Endorsement effects in large language models2h◆AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders2h◆Are LLMs Ready to Assist Physicians? PhysAssistBench for Interactive Doctor-Patient-EHR Assistance2h◆Lost in Backpropagation: The LM Head is a Gradient Bottleneck2h◆RELISH: LLM REgression with a Latent Iterative State Head2h◆LionVote: Per-Layer Learning Rate Adaptation for Lion2h◆Spectrally Deconfounded Gradient Boosting2h◆Terminal Dimension Reduction for Time Series with Applications2h◆The Count Is There, but Misaligned: Understanding and Correcting Counting Failures in VLMs2h◆Lipschitz-Based Robustness Certification Under Floating-Point Execution2h◆Fine-grained Soundscape Control for Augmented Hearing2h◆SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets2h◆CogniConsole: Externalizing Inference-Time Control as a Formal Abstraction for Reliable LLM Interactions2h◆GATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning2h◆Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation2h◆Video Generation Models are General-Purpose Vision Learners2h◆Evolutionary Intelligence for Scientific Discovery: From Evolutionary Computation to Cumulative Discovery Systems2h◆ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models2h◆A Personalized Computational Framework for Assessing the Sufficiency of Partially Observed Data in Healthcare AI models2h◆Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations2h◆Geopolitical alignment: Endorsement effects in large language models2h◆AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders2h◆Are LLMs Ready to Assist Physicians? PhysAssistBench for Interactive Doctor-Patient-EHR Assistance2h◆Lost in Backpropagation: The LM Head is a Gradient Bottleneck2h◆RELISH: LLM REgression with a Latent Iterative State Head2h◆LionVote: Per-Layer Learning Rate Adaptation for Lion2h◆Spectrally Deconfounded Gradient Boosting2h◆Terminal Dimension Reduction for Time Series with Applications2h◆The Count Is There, but Misaligned: Understanding and Correcting Counting Failures in VLMs2h◆Lipschitz-Based Robustness Certification Under Floating-Point Execution2h◆Fine-grained Soundscape Control for Augmented Hearing2h◆
News/RL Excursions during Pre-Training: Re-examining Policy Optimization for LLM training
arxiv
PublishedJune 4, 2026 at 4:00 AM
▲bullish

RL Excursions during Pre-Training: Re-examining Policy Optimization for LLM training

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.04272v1 Announce Type: new Abstract: The standard LLM training pipeline applies reinforcement learning (RL) only after pre-training and supervised fine-tuning (SFT). We question this status quo by training a LLM from scratch and applying RL, SFT, and SFT followed by RL directly to interme

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#reinforcement-learning#pre-training#fine-tuning#llm

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#reinforcement-learning#pre-training#fine-tuning#llm

Related coverage

More from ARXIV
arxivSolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets2harxivCogniConsole: Externalizing Inference-Time Control as a Formal Abstraction for Reliable LLM Interactions2harxivGATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning2harxivAgora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation2h
The Bubble Brief
WEEKLY

Read reinforcement-learning insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews