·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Democrats think cracking down on AI could give them an edge1h◆AI researchers stranded Claude on a virtual desert island. It started to “play.”4h◆Rethinking Negative Prompting for Concept Suppression: Unconditional Anchoring and Localized Guidance9h◆Safety Alignment via Constrained Knowledge Unlearning9h◆Cross-Layer Attention Probing for Fine-Grained Hallucination Detection9h◆AQUA-Net: Adaptive Frequency Fusion and Illumination Aware Network for Underwater Image Enhancement9h◆Schr\"odinger's Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation9h◆Veri-Sure: Multi-Agent RTL Code Generation with Temporal Tracing, Slicing and Formal Verification9h◆Happy Young Women, Grumpy Old Men? Emotion-Driven Demographic Biases in Synthetic Face Generation9h◆Inter- and Intra-Subject Variability in EEG: A Systematic Survey9h◆A Theoretical Analysis of Test-Driven Code Generation9h◆Scalable Adaptation of 3D Geometric Foundation Models via Weak Supervision from Internet Video9h◆Language Model Goal Selection Differs from Humans' in a Self-Directed Learning Task9h◆AOI: Turning Failed Trajectories into Training Signals for Autonomous Cloud Diagnosis9h◆M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling9h◆Can Graph Learning Learn Circuits?9h◆Beyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery9h◆Suppression of $^{14}\mathrm{C}$ photon hits in large liquid scintillator detectors via spatiotemporal deep learning9h◆OmniTrace: A Unified Framework for Generation-Time Source Attribution in Omni-Modal LLMs9h◆MOSAIC: Module Discovery via Sparse Additive Identifiable Causal Learning for Scientific Time Series9h◆Democrats think cracking down on AI could give them an edge1h◆AI researchers stranded Claude on a virtual desert island. It started to “play.”4h◆Rethinking Negative Prompting for Concept Suppression: Unconditional Anchoring and Localized Guidance9h◆Safety Alignment via Constrained Knowledge Unlearning9h◆Cross-Layer Attention Probing for Fine-Grained Hallucination Detection9h◆AQUA-Net: Adaptive Frequency Fusion and Illumination Aware Network for Underwater Image Enhancement9h◆Schr\"odinger's Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation9h◆Veri-Sure: Multi-Agent RTL Code Generation with Temporal Tracing, Slicing and Formal Verification9h◆Happy Young Women, Grumpy Old Men? Emotion-Driven Demographic Biases in Synthetic Face Generation9h◆Inter- and Intra-Subject Variability in EEG: A Systematic Survey9h◆A Theoretical Analysis of Test-Driven Code Generation9h◆Scalable Adaptation of 3D Geometric Foundation Models via Weak Supervision from Internet Video9h◆Language Model Goal Selection Differs from Humans' in a Self-Directed Learning Task9h◆AOI: Turning Failed Trajectories into Training Signals for Autonomous Cloud Diagnosis9h◆M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling9h◆Can Graph Learning Learn Circuits?9h◆Beyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery9h◆Suppression of $^{14}\mathrm{C}$ photon hits in large liquid scintillator detectors via spatiotemporal deep learning9h◆OmniTrace: A Unified Framework for Generation-Time Source Attribution in Omni-Modal LLMs9h◆MOSAIC: Module Discovery via Sparse Additive Identifiable Causal Learning for Scientific Time Series9h◆
News/Teach the Magnitude, Not the Direction: Verifier-Anchored Credit Assignment for Multi-Turn Multi-Step LLM Agents
arxiv
PublishedOctober 19, 2026 at 4:00 AM

Teach the Magnitude, Not the Direction: Verifier-Anchored Credit Assignment for Multi-Turn Multi-Step LLM Agents

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

arXiv:2608.13179v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) directly optimizes verifier-defined outcomes for multi-turn tool-use agents, yet its trajectory-level credit assignment conflates heterogeneous per-turn outcomes into a single reward signal. On-

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

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

Your email is used only to send The Bubble Brief. Privacy policy

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
arxivRethinking Negative Prompting for Concept Suppression: Unconditional Anchoring and Localized Guidance9harxivSafety Alignment via Constrained Knowledge Unlearning9harxivCross-Layer Attention Probing for Fine-Grained Hallucination Detection9harxivAQUA-Net: Adaptive Frequency Fusion and Illumination Aware Network for Underwater Image Enhancement9h
The Bubble Brief
MON · WED · FRI

Read AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

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

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews