Pith. sign in

REVIEW 10 cited by

OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2410.09671 v1 pith:V27JMQSD submitted 2024-10-12 cs.AI cs.CL

Jun Wang , Meng Fang , Ziyu Wan , Muning Wen , Jiachen Zhu , Anjie Liu , Ziqin Gong , Yan Song
show 5 more authors
Lei Chen Lionel M. Ni Linyi Yang Ying Wen Weinan Zhang
This is my paper
classification cs.AIcs.CL
keywords reasoningopenrlearningreinforcementframeworkmodelsopen-sourceadvanced
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In this technical report, we introduce OpenR, an open-source framework designed to integrate key components for enhancing the reasoning capabilities of large language models (LLMs). OpenR unifies data acquisition, reinforcement learning training (both online and offline), and non-autoregressive decoding into a cohesive software platform. Our goal is to establish an open-source platform and community to accelerate the development of LLM reasoning. Inspired by the success of OpenAI's o1 model, which demonstrated improved reasoning abilities through step-by-step reasoning and reinforcement learning, OpenR integrates test-time compute, reinforcement learning, and process supervision to improve reasoning in LLMs. Our work is the first to provide an open-source framework that explores the core techniques of OpenAI's o1 model with reinforcement learning, achieving advanced reasoning capabilities beyond traditional autoregressive methods. We demonstrate the efficacy of OpenR by evaluating it on the MATH dataset, utilising publicly available data and search methods. Our initial experiments confirm substantial gains, with relative improvements in reasoning and performance driven by test-time computation and reinforcement learning through process reward models. The OpenR framework, including code, models, and datasets, is accessible at https://openreasoner.github.io.

Discussion (0). Sign in to comment.

Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Stepwise Reasoning Checkpoint Analysis: A Test Time Scaling Method to Enhance LLMs' Reasoning

    cs.CL 2025-05 conditional novelty 7.0 of 10

    A checkpoint-based search and candidate augmentation method improves small LLM mathematical reasoning accuracy over existing test-time scaling baselines.

  2. Training Language Models to Cooperate with Inference-Time Controllers

    cs.AI 2026-07 conditional novelty 6.5 of 10

    Multi-controller post-training with turn-level GRPO over reusable reasoning modules improves LLM transfer to held-out and shifted inference-time controllers versus single-controller RL.

  3. Knowledge Graph Augmented Large Language Models for Disease Prediction

    cs.AI 2025-12 conditional novelty 6.0 of 10

    A knowledge-graph-guided chain-of-thought fine-tuning pipeline for EHR disease prediction improves accuracy on transfer and clinician-rated explanation quality, but only modestly improves ranking metrics.

  4. Error Typing for Smarter Rewards: Improving Process Reward Models with Error-Aware Hierarchical Supervision

    cs.CL 2025-05 conditional novelty 6.0 of 10

    A hierarchical process reward model that classifies math and consistency errors before scoring steps achieves the best PRMBench result among discriminative PRMs and improves reward-guided math search.

  5. Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Guided by Gut lets an LLM guide its own search using token-level confidence and step novelty, with a RL fine-tuning step to calibrate that confidence, achieving PRM-level accuracy at far lower compute and memory.

  6. Typhoon T1: An Open Thai Reasoning Model

    cs.CL 2025-02 conditional novelty 6.0 of 10

    Structured long-thinking SFT turns a 3B Thai instruct model into a reasoning model that improves on several English benchmarks and can think in Thai, with a fully open recipe.

  7. Faster and Better LLMs via Latency-Aware Test-Time Scaling

    cs.CL 2025-05 conditional novelty 5.0 of 10

    Test-time scaling should be tuned for wall-clock latency rather than token count; branches plus speculative decoding win under time budgets.

  8. A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models

    cs.CL 2025-09 conditional novelty 4.0 of 10

    A structured literature survey concluding that reasoning capabilities do not automatically make LLMs more trustworthy and can introduce new vulnerabilities in safety, robustness, and privacy.

  9. Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models

    cs.CL 2025-07 reject novelty 4.0 of 10

    A step-level verifier-guided hybrid of Best-of-N sampling, Monte Carlo tree search, and conditional self-refinement improves reasoning in small instruction-tuned LLMs, claiming up to 28.6-point gains.

  10. Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL

    cs.CL 2025-05 conditional novelty 4.0 of 10

    Medical QA accuracy improves substantially through reinforcement learning with a binary correct-answer reward alone, without supervised fine-tuning on distilled reasoning traces.

Pith tools