Cover Image

Blockwise Advantage Estimation for Multi-Objective RL with Verifiable Rewards

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Pavlenko, Kirill, Golubev, Alexander, Karasik, Simon, Yangel, Boris
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards
by: Li, Ming, et al.
Published: (2025)

Exploring and Improving Drafts in Blockwise Parallel Decoding
by: Kim, Taehyeon, et al.
Published: (2024)

Simultaneous Multi-objective Alignment Across Verifiable and Non-verifiable Rewards
by: Shen, Yiran, et al.
Published: (2025)

BlockCert: Certified Blockwise Extraction of Transformer Mechanisms
by: Andric, Sandro
Published: (2025)

Reward-free Alignment for Conflicting Objectives
by: Chen, Peter, et al.
Published: (2026)

Exploring RL-based LLM Training for Formal Language Tasks with Programmed Rewards
by: Padula, Alexander G., et al.
Published: (2024)

Boosting Reinforcement Learning with Verifiable Rewards via Randomly Selected Few-Shot Guidance
by: Yan, Kai, et al.
Published: (2026)

Mitigating Reward Hacking in RLHF via Advantage Sign Robustness
by: Ono, Shinnosuke, et al.
Published: (2026)

RL Tango: Reinforcing Generator and Verifier Together for Language Reasoning
by: Zha, Kaiwen, et al.
Published: (2025)

Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains
by: Gunjal, Anisha, et al.
Published: (2025)

FlowRL: Matching Reward Distributions for LLM Reasoning
by: Zhu, Xuekai, et al.
Published: (2025)

ToolRL: Reward is All Tool Learning Needs
by: Qian, Cheng, et al.
Published: (2025)

Gradient Regularization Prevents Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards
by: Ackermann, Johannes, et al.
Published: (2026)

BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation
by: Xu, Peng, et al.
Published: (2024)

BeamPERL: Parameter-Efficient RL with Verifiable Rewards Specializes Compact LLMs for Structured Beam Mechanics Reasoning
by: Hage, Tarjei Paule, et al.
Published: (2026)

Adaptive Rollout Allocation for Online Reinforcement Learning with Verifiable Rewards
by: Nguyen, Hieu Trung, et al.
Published: (2026)

Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards
by: Wang, Haoxiang, et al.
Published: (2024)

REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewards
by: Stojanovski, Zafir, et al.
Published: (2025)

On Designing Effective RL Reward at Training Time for LLM Reasoning
by: Gao, Jiaxuan, et al.
Published: (2024)

RewardFlow: Topology-Aware Reward Propagation on State Graphs for Agentic RL with Large Language Models
by: Feng, Xiao, et al.
Published: (2026)

Masked-and-Reordered Self-Supervision for Reinforcement Learning from Verifiable Rewards
by: Wang, Zhen, et al.
Published: (2025)

Reinforce-Ada: An Adaptive Sampling Framework under Non-linear RL Objectives
by: Xiong, Wei, et al.
Published: (2025)

Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards
by: Ma, Zhengzhao, et al.
Published: (2026)

Rate or Fate? RLV$^\varepsilon$R: Reinforcement Learning with Verifiable Noisy Rewards
by: Rad, Ali, et al.
Published: (2026)

Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning
by: Fei, Wu, et al.
Published: (2025)

RLBFF: Binary Flexible Feedback to bridge between Human Feedback & Verifiable Rewards
by: Wang, Zhilin, et al.
Published: (2025)

Birdie: Advancing State Space Models with Reward-Driven Objectives and Curricula
by: Blouir, Sam, et al.
Published: (2024)

Training Long-Context, Multi-Turn Software Engineering Agents with Reinforcement Learning
by: Golubev, Alexander, et al.
Published: (2025)

A Relative-Budget Theory for Reinforcement Learning with Verifiable Rewards in Large Language Model Reasoning
by: Wachi, Akifumi, et al.
Published: (2026)

GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL
by: Yang, Rui, et al.
Published: (2026)

CoVerRL: Breaking the Consensus Trap in Label-Free Reasoning via Generator-Verifier Co-Evolution
by: Pan, Teng, et al.
Published: (2026)

Language Models as Efficient Reward Function Searchers for Custom-Environment Multi-Objective Reinforcement
by: Xie, Guanwen, et al.
Published: (2024)

SHAPE: Stage-aware Hierarchical Advantage via Potential Estimation for LLM Reasoning
by: Ai, Zhengyang, et al.
Published: (2026)

Multi-Turn Code Generation Through Single-Step Rewards
by: Jain, Arnav Kumar, et al.
Published: (2025)

Verifying the Verifiers: Unveiling Pitfalls and Potentials in Fact Verifiers
by: Seo, Wooseok, et al.
Published: (2025)

On the Ability of Transformers to Verify Plans
by: Sarrof, Yash, et al.
Published: (2026)

In-Context Representation Hijacking
by: Yona, Itay, et al.
Published: (2025)

SCI-Verifier: Scientific Verifier with Thinking
by: Zheng, Shenghe, et al.
Published: (2025)

Multi-Objective Large Language Model Unlearning
by: Pan, Zibin, et al.
Published: (2024)

Pareto Multi-Objective Alignment for Language Models
by: He, Qiang, et al.
Published: (2025)