·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Why AMI Labs’ Alexandre LeBrun won’t call his AI ‘AGI’ or ‘superintelligence’58m◆Moonshot’s upcoming Kimi 3 is expected to close the gap with Anthropic’s Opus 4.81h◆Apple Intelligence approved for launch in China with Alibaba and Baidu2h◆Claude can now use your 1Password credentials for you2h◆Google ordered to open Android and Search to rivals in Europe3h◆Newer Models, Same Advantage3h◆Computer cops4h◆Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling11h◆SemaDiff: Identifying Semantic-Changing Commits with Generated Code and Tests11h◆MASPRM: Multi-Agent System Process Reward Model11h◆Delving into the Temporal Challenges of Unified Video Protection Against Image-to-Video and Fine-Tuning-based Customization11h◆A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping11h◆FastCentNN: Accelerating Centroid Neural Network with Entropy Proxy11h◆Cortical-SSM: A Deep State Space Model for Motor Imagery Decoding from EEG Signals11h◆Avoiding unsafe sets when training with Langevin Dynamics11h◆Set-shifting Behavioral Test for Harnessed Agents11h◆AIMO Interpretability Challenge11h◆The Perplexity Trap: When Patent Law Makes Human Writing Look Like AI11h◆The Refusal Residue: When Probes Catch Alignment Faking and When They Don't11h◆ECHO: Prune To Act, Trace To Learn With Selective Turn Memory In Agentic RL11h◆Why AMI Labs’ Alexandre LeBrun won’t call his AI ‘AGI’ or ‘superintelligence’58m◆Moonshot’s upcoming Kimi 3 is expected to close the gap with Anthropic’s Opus 4.81h◆Apple Intelligence approved for launch in China with Alibaba and Baidu2h◆Claude can now use your 1Password credentials for you2h◆Google ordered to open Android and Search to rivals in Europe3h◆Newer Models, Same Advantage3h◆Computer cops4h◆Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling11h◆SemaDiff: Identifying Semantic-Changing Commits with Generated Code and Tests11h◆MASPRM: Multi-Agent System Process Reward Model11h◆Delving into the Temporal Challenges of Unified Video Protection Against Image-to-Video and Fine-Tuning-based Customization11h◆A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping11h◆FastCentNN: Accelerating Centroid Neural Network with Entropy Proxy11h◆Cortical-SSM: A Deep State Space Model for Motor Imagery Decoding from EEG Signals11h◆Avoiding unsafe sets when training with Langevin Dynamics11h◆Set-shifting Behavioral Test for Harnessed Agents11h◆AIMO Interpretability Challenge11h◆The Perplexity Trap: When Patent Law Makes Human Writing Look Like AI11h◆The Refusal Residue: When Probes Catch Alignment Faking and When They Don't11h◆ECHO: Prune To Act, Trace To Learn With Selective Turn Memory In Agentic RL11h◆
News/Truncated Step-Level Sampling with Process Rewards for Retrieval-Augmented Reasoning
arxiv
PublishedJuly 11, 2026 at 4:00 AM
—neutral

Truncated Step-Level Sampling with Process Rewards for Retrieval-Augmented Reasoning

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

arXiv:2602.23440v4 Announce Type: replace Abstract: Reinforcement learning has emerged as an effective paradigm for training large language models to interleave reasoning with search engine calls. However, existing approaches face a fundamental credit assignment problem: methods like Search-R1 assig

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 →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivMulti-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling11harxivSemaDiff: Identifying Semantic-Changing Commits with Generated Code and Tests11harxivMASPRM: Multi-Agent System Process Reward Model11harxivDelving into the Temporal Challenges of Unified Video Protection Against Image-to-Video and Fine-Tuning-based Customization11h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

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

Originally published on arxiv ↗
HomeModelsNews