:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Aggarwal, Pranjal, Kim, Seungone, Lanchantin, Jack, Welleck, Sean, Weston, Jason, Kulikov, Ilia, Saha, Swarnadeep
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Machine Learning
Online Access:	https://arxiv.org/abs/2508.13141
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning
by: Aggarwal, Pranjal, et al.
Published: (2025)

J1: Incentivizing Thinking in LLM-as-a-Judge via Reinforcement Learning
by: Whitehouse, Chenxi, et al.
Published: (2025)

The Majority is not always right: RL training for solution aggregation
by: Zhao, Wenting, et al.
Published: (2025)

Jointly Reinforcing Diversity and Quality in Language Model Generations
by: Li, Tianjian, et al.
Published: (2025)

Hybrid Reinforcement: When Reward Is Sparse, It's Better to Be Dense
by: Tao, Leitian, et al.
Published: (2025)

Programming with Pixels: Can Computer-Use Agents do Software Engineering?
by: Aggarwal, Pranjal, et al.
Published: (2025)

Agentic-R1: Distilled Dual-Strategy Reasoning
by: Du, Weihua, et al.
Published: (2025)

LLM Pretraining with Continuous Concepts
by: Tack, Jihoon, et al.
Published: (2025)

Reasoning over mathematical objects: on-policy reward modeling and test time aggregation
by: Aggarwal, Pranjal, et al.
Published: (2026)

Bridging Offline and Online Reinforcement Learning for LLMs
by: Lanchantin, Jack, et al.
Published: (2025)

AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement
by: Aggarwal, Pranjal, et al.
Published: (2024)

Gym-Anything: Turn any Software into an Agent Environment
by: Aggarwal, Pranjal, et al.
Published: (2026)

Branch-Solve-Merge Improves Large Language Model Evaluation and Generation
by: Saha, Swarnadeep, et al.
Published: (2023)

SPICE: Self-Play In Corpus Environments Improves Reasoning
by: Liu, Bo, et al.
Published: (2025)

Self-Improving Pretraining: using post-trained models to pretrain better models
by: Tan, Ellen Xiaoqing, et al.
Published: (2026)

Adaptive Decoding via Latent Preference Optimization
by: Dhuliawala, Shehzaad, et al.
Published: (2024)

Diverse Preference Optimization
by: Lanchantin, Jack, et al.
Published: (2025)

From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models
by: Welleck, Sean, et al.
Published: (2024)

Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge
by: Saha, Swarnadeep, et al.
Published: (2025)

Verus-SpecGym: An Agentic Environment for Evaluating Specification Autoformalization
by: Agarwal, Anmol, et al.
Published: (2026)

ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs
by: Chen, Justin Chih-Yao, et al.
Published: (2023)

CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks
by: Yu, Ping, et al.
Published: (2025)

miniCTX: Neural Theorem Proving with (Long-)Contexts
by: Hu, Jiewen, et al.
Published: (2024)

Optimizing Temperature for Language Models with Multi-Sample Inference
by: Du, Weihua, et al.
Published: (2025)

Distilling System 2 into System 1
by: Yu, Ping, et al.
Published: (2024)

NaturalThoughts: Selecting and Distilling Reasoning Traces for General Reasoning Tasks
by: Li, Yang, et al.
Published: (2025)

RefineBench: Evaluating Refinement Capability of Language Models via Checklists
by: Lee, Young-Jun, et al.
Published: (2025)

SmartSwitch: Advancing LLM Reasoning by Overcoming Underthinking via Promoting Deeper Thought Exploration
by: Zhang, Xichen, et al.
Published: (2025)

GradAlign: Gradient-Aligned Data Selection for LLM Reinforcement Learning
by: Yang, Ningyuan, et al.
Published: (2026)

Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs
by: Wang, Yue, et al.
Published: (2025)

ImProver 2: Iteratively Self-Improving LMs for Neurosymbolic Proof Optimization
by: Ahuja, Riyaz, et al.
Published: (2026)

RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs
by: Chaudhari, Shreyas, et al.
Published: (2024)

AgentBench: Evaluating LLMs as Agents
by: Liu, Xiao, et al.
Published: (2023)

Evaluating LLMs' Reasoning Over Ordered Procedural Steps
by: Anika, Adrita, et al.
Published: (2025)

Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
by: Kim, Seungone, et al.
Published: (2023)

System-1.x: Learning to Balance Fast and Slow Planning with Language Models
by: Saha, Swarnadeep, et al.
Published: (2024)

Post-training an LLM for RAG? Train on Self-Generated Demonstrations
by: Finlayson, Matthew, et al.
Published: (2025)

ThinkEdit: Interpretable Weight Editing to Mitigate Overly Short Thinking in Reasoning Models
by: Sun, Chung-En, et al.
Published: (2025)

From Solver to Tutor: Evaluating the Pedagogical Intelligence of LLMs with KMP-Bench
by: Shi, Weikang, et al.
Published: (2026)

Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision
by: Sun, Zhiqing, et al.
Published: (2024)