Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards | Paper Citation Record

Observation 9c2a8297-13ac-4bdb-9160-cda16609d97a · outbound

This paper cites American Mathematical Society, 2 edition, 2010.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards American Mathematical Society, 2 edition, 2010

Reference 1

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This paper cites Cambridge university press, 2002.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Cambridge university press, 2002

Reference 2

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This paper cites Springer, 1994.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Springer, 1994

Reference 3

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This paper cites SIAM, 2000.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards SIAM, 2000

Reference 4

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This paper cites SIAM, 1998.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards SIAM, 1998

Reference 5

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This paper cites Evaluating Large Language Models Trained on Code.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Evaluating Large Language Models Trained on Code

Reference 6

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This paper cites Measuring Coding Challenge Competence With APPS.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Measuring Coding Challenge Competence With APPS

Reference 7

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This paper cites Program Synthesis with Large Language Models.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Program Synthesis with Large Language Models

Reference 8

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This paper cites Coderl: Mastering code generation through pretrained models and deep reinforcement learning.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Coderl: Mastering code generation through pretrained models and deep reinforcement learning

Reference 9

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Observation 130a9fb1-6740-4829-bbfe-fd750d70b16c · outbound

This paper cites Narasimhan, and Yuan Cao.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Narasimhan, and Yuan Cao

Reference 10

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This paper cites URLhttps://openreview.net/forum?id=WE_vluYUL-X.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards URLhttps://openreview.net/forum?id=WE_vluYUL-X

Reference 11

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This paper cites RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning

Reference 12

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This paper cites Codepde: An inference framework for llm-driven PDE solver generation.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Codepde: An inference framework for llm-driven PDE solver generation

Reference 13

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This paper cites SciML Agents: Write the Solver, Not the Solution.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards SciML Agents: Write the Solver, Not the Solution

Reference 14

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This paper cites Autonumerics: An autonomous, pde-agnostic multi-agent pipeline for scientific computing.arXiv preprint arXiv:2602.17607, 2026.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Autonumerics: An autonomous, pde-agnostic multi-agent pipeline for scientific computing.arXiv preprint arXiv:2602.17607, 2026

Reference 15

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This paper cites All-fem: Agentic large language models fine-tuned for finite element methods.Computer Methods in Applied Mechanics and Engineering, 457:118985, 2026.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards All-fem: Agentic large language models fine-tuned for finite element methods.Computer Methods in Applied Mechanics and Engineering, 457:118985, 2026

Reference 16

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This paper cites Pde-agent: A toolchain-augmented multi-agent framework for pde solving.arXiv preprint arXiv:2512.16214, 2025.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Pde-agent: A toolchain-augmented multi-agent framework for pde solving.arXiv preprint arXiv:2512.16214, 2025

Reference 17

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This paper cites PINNsAgent: Automated PDE Surrogation with Large Language Models.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards PINNsAgent: Automated PDE Surrogation with Large Language Models

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This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 19

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This paper cites Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models

Reference 20

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This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 21

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Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Unresolved cited work

Reference 22

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Observation 39fc4ebd-d77d-44b6-adb1-6c1a7d4acaf5 · outbound

This paper cites Solving math word problems with process- and outcome-based feedback.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Solving math word problems with process- and outcome-based feedback

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Observation 4d811320-eb90-4cd9-a7f8-44200d21492a · outbound

This paper cites Efficient memory management for large language model serving with pagedattention.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Efficient memory management for large language model serving with pagedattention

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This paper cites Hybridflow: A flexible and efficient rlhf framework.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Hybridflow: A flexible and efficient rlhf framework

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Observation 45d4cca4-fa63-48b2-bc1a-8874f9d0d396 · outbound

This paper cites Huerta, and Hao Peng.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Huerta, and Hao Peng

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This paper cites Foam-agent: Towards automated intelligent cfd workflows.arXiv preprint arXiv:2505.04997, 2025.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Foam-agent: Towards automated intelligent cfd workflows.arXiv preprint arXiv:2505.04997, 2025

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This paper cites Openfoamgpt: A retrieval-augmented large language model (llm) agent for openfoam-based computational fluid dynamics.Physics of Fluids, 37(3), 2025.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Openfoamgpt: A retrieval-augmented large language model (llm) agent for openfoam-based computational fluid dynamics.Physics of Fluids, 37(3), 2025

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This paper cites MetaOpenFOAM: an LLM-based multi-agent framework for CFD.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards MetaOpenFOAM: an LLM-based multi-agent framework for CFD

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This paper cites Mooseagent: A llm based multi-agent framework for automating moose simulation, 2025.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Mooseagent: A llm based multi-agent framework for automating moose simulation, 2025

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Observation e38baeff-07f0-4af1-800e-ec6d11f182e2 · outbound

This paper cites Chronollm: customizing language models for physics-based simulation code generation.Multibody System Dynamics, Feb 2026.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Chronollm: customizing language models for physics-based simulation code generation.Multibody System Dynamics, Feb 2026

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Observation 913d1ec4-cdfa-4feb-a229-e36b065e6a8d · outbound

This paper cites Lang- pinn: From language to physics-informed neural networks via a multi-agent framework.arXiv preprint arXiv:2510.05158, 2025.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Lang- pinn: From language to physics-informed neural networks via a multi-agent framework.arXiv preprint arXiv:2510.05158, 2025

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This paper cites FEABench: Evaluating Language Models on Multiphysics Reasoning Ability.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards FEABench: Evaluating Language Models on Multiphysics Reasoning Ability

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Observation 72c4e00f-6067-4c45-a5a7-e4c55ce47623 · outbound

This paper cites Solving Physics Olympiad via Reinforcement Learning on Physics Simulators.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Solving Physics Olympiad via Reinforcement Learning on Physics Simulators

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Observation 1359ee21-87e0-4e85-985e-3ec70721d6e9 · outbound

This paper cites PDE-Controller: LLMs for Autoformalization and Reasoning of PDEs.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards PDE-Controller: LLMs for Autoformalization and Reasoning of PDEs

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This paper cites Agentic scientific simulation: Execution-grounded model construction and reconstruction.arXiv preprint arXiv:2603.00214, 2026.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Agentic scientific simulation: Execution-grounded model construction and reconstruction.arXiv preprint arXiv:2603.00214, 2026

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This paper cites DAPO: An open-source LLM reinforcement learning system at scale.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards DAPO: An open-source LLM reinforcement learning system at scale

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This paper cites Group Sequence Policy Optimization.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Group Sequence Policy Optimization

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This paper cites Stepcoder: improving code generation with reinforcement learning from compiler feedback.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Stepcoder: improving code generation with reinforcement learning from compiler feedback

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This paper cites Reinforcement Learning for Machine Learning Engineering Agents.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Reinforcement Learning for Machine Learning Engineering Agents

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This paper cites Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty

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Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Unresolved cited work

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Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Fourier Neural Operator for Parametric Partial Differential Equations

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This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Learning nonlinear operators via deeponet based on the universal approximation theorem of operators

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Observation 3a8f95d5-ad38-4ff7-b096-65755577ef78 · outbound

This paper cites Towards long rollout of neural operators with local attention and flow matching-inspired correction: An example in frontal polymerization pdes.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Towards long rollout of neural operators with local attention and flow matching-inspired correction: An example in frontal polymerization pdes

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This paper cites Pdebench: An extensive benchmark for scientific machine learning.Advances in neural information processing systems, 35:1596–1611, 2022.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Pdebench: An extensive benchmark for scientific machine learning.Advances in neural information processing systems, 35:1596–1611, 2022

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This paper cites Diffusionpde: Generative pde-solving under partial observation.Advances in Neural Information Processing Systems, 37: 130291–130323, 2024.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Diffusionpde: Generative pde-solving under partial observation.Advances in Neural Information Processing Systems, 37: 130291–130323, 2024

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Observation 546f6ddc-d397-4231-b52c-47b2f65662d5 · outbound

This paper cites Physics-Informed Diffusion Models.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Physics-Informed Diffusion Models

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Observation 0e93baef-bf6f-4707-b999-08b1e1e67819 · outbound

This paper cites Physics-constrained flow matching: Sampling generative models with hard constraints.arXiv preprint arXiv:2506.04171, 2025.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Physics-constrained flow matching: Sampling generative models with hard constraints.arXiv preprint arXiv:2506.04171, 2025

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This paper cites End-to-end probabilistic framework for learning with hard constraints.arXiv preprint arXiv:2506.07003, 2025.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards End-to-end probabilistic framework for learning with hard constraints.arXiv preprint arXiv:2506.07003, 2025

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Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Training language models to follow instructions with human feedback

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This paper cites Qwen2.5-Coder Technical Report.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Qwen2.5-Coder Technical Report

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards LoRA: Low-Rank Adaptation of Large Language Models

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Observation 18f47ed7-4f88-4f12-8d82-8395aff5f1f8 · outbound

This paper cites A model for fast computer simulation of waves in excitable media.Physica D: Nonlinear Phenomena, 49(1-2):61–70, 1991.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards A model for fast computer simulation of waves in excitable media.Physica D: Nonlinear Phenomena, 49(1-2):61–70, 1991

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Observation 29a80986-4a9a-47be-8f06-bdae568ae946 · outbound

This paper cites Finite difference method for numerical computation of discontinuous solutions of the equations of fluid dynamics.Matematiˇ ceskij sbornik, 47(3): 271–306, 1959.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Finite difference method for numerical computation of discontinuous solutions of the equations of fluid dynamics.Matematiˇ ceskij sbornik, 47(3): 271–306, 1959

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Observation 262ef592-3736-4460-b7eb-b996676d64bf · outbound

This paper cites Towards the ultimate conservative difference scheme.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Towards the ultimate conservative difference scheme

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Observation b70b2f42-e862-4aeb-bc88-0fb87d62d5af · outbound

This paper cites New high-resolution central schemes for nonlinear conservation laws and convection–diffusion equations.Journal of computational physics, 160 (1):241–282, 2000.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards New high-resolution central schemes for nonlinear conservation laws and convection–diffusion equations.Journal of computational physics, 160 (1):241–282, 2000

Reference 57

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edc2e5e0-c850-4b61-8191-2e4d0b06feda · outbound

This paper cites Toro.Riemann Solvers and Numerical Methods for Fluid Dynamics.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Toro.Riemann Solvers and Numerical Methods for Fluid Dynamics

Reference 58

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47019da4-0458-4c15-9f9a-eb857fe302e9 · outbound

This paper cites Strong stability-preserving high-order time discretization methods.SIAM review, 43(1):89–112, 2001.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Strong stability-preserving high-order time discretization methods.SIAM review, 43(1):89–112, 2001

Reference 59

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a4015b7-9d4c-4cd6-b243-f6e25b2ba310 · outbound

This paper cites On the construction and comparison of difference schemes.SIAM journal on numerical analysis, 5(3):506–517, 1968.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards On the construction and comparison of difference schemes.SIAM journal on numerical analysis, 5(3):506–517, 1968

Reference 60

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a52ddfc9-a4ff-4487-b268-7fc131ab12d8 · outbound

This paper cites Implicit-explicit runge-kutta methods for time-dependent partial differential equations.Applied Numerical Mathematics, 25(2-3): 151–167, 1997.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Implicit-explicit runge-kutta methods for time-dependent partial differential equations.Applied Numerical Mathematics, 25(2-3): 151–167, 1997

Reference 61

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 468f879b-6e28-49e1-811b-476cd6f91fd3 · outbound

This paper cites Fourth-order time-stepping for stiff pdes.SIAM Journal on Scientific Computing, 26(4):1214–1233, 2005.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Fourth-order time-stepping for stiff pdes.SIAM Journal on Scientific Computing, 26(4):1214–1233, 2005

Reference 62

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0eeb148-5fcc-417f-8072-fb4fafdb1ba3 · outbound

This paper cites The numerical solution of the Navier–Stokes equations for an incom- pressible fluid.Bulletin of the American Mathematical Society, 73(6):928–931, 1967.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards The numerical solution of the Navier–Stokes equations for an incom- pressible fluid.Bulletin of the American Mathematical Society, 73(6):928–931, 1967

Reference 63

Resolution: verified exact; doi, observed 2026-07-14T11:30:25.737831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c68f4517-646a-46cd-b18f-0f38ebd31974 · outbound

This paper cites Wellesley-Cambridge Press, 1986.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Wellesley-Cambridge Press, 1986

Reference 64

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c58c301-050a-439f-a395-ad8ceb47c94f · outbound

This paper cites Scipy 1.0: fundamental algorithms for scientific computing in python.Nature methods, 17(3):261–272, 2020.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Scipy 1.0: fundamental algorithms for scientific computing in python.Nature methods, 17(3):261–272, 2020

Reference 65

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffb451eb-eecf-46eb-b60e-701e506d4255 · outbound

This paper cites On the elimination of aliasing in finite-difference schemes by filtering high-wavenumber components.Journal of Atmospheric Sciences, 28(6):1074–1074, 1971.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards On the elimination of aliasing in finite-difference schemes by filtering high-wavenumber components.Journal of Atmospheric Sciences, 28(6):1074–1074, 1971

Reference 66

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68bbd1b8-127d-4493-8bbb-b0d3b0c5a434 · outbound

This paper cites Semi-lagrangian integration schemes for atmospheric models—a review.Monthly weather review, 119(9):2206–2223, 1991.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Semi-lagrangian integration schemes for atmospheric models—a review.Monthly weather review, 119(9):2206–2223, 1991

Reference 67

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4458a449-2ca6-4396-9a45-40c8850cd024 · outbound

This paper cites SIAM, 2007.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards SIAM, 2007

Reference 68

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cccf25a3-36ab-4c17-9633-25e48b97dfd3 · outbound

This paper cites The calculation of the interaction of non-stationary shock waves and obstacles.USSR Computational Mathematics and Mathematical Physics, 1(2):304–320, 1962.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards The calculation of the interaction of non-stationary shock waves and obstacles.USSR Computational Mathematics and Mathematical Physics, 1(2):304–320, 1962

Reference 69

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2826c273-9ea2-42ed-b809-f5e36decb625 · outbound

This paper cites Systems of conservation laws.Communications on Pure and Applied Mathematics, 13:217–237, 1960.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Systems of conservation laws.Communications on Pure and Applied Mathematics, 13:217–237, 1960

Reference 70

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3bceee2-ffed-4c61-98dc-29d9215b16fb · outbound

This paper cites The effect of viscosity in hypervelocity impact cratering.Journal of spacecraft and rockets, 40(5):757–763, 2003.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards The effect of viscosity in hypervelocity impact cratering.Journal of spacecraft and rockets, 40(5):757–763, 2003

Reference 71

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df339ee1-c52c-457b-bd41-89605c43c313 · outbound

This paper cites Springer, 2003.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Springer, 2003

Reference 72

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d93d8694-048c-474b-b93d-9c9e2cdedd53 · outbound

This paper cites Finite volume methods.Handbook of numerical analysis, 7:713–1018, 2000.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Finite volume methods.Handbook of numerical analysis, 7:713–1018, 2000

Reference 73

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdda3a8f-19cc-4c8d-b1da-891fd4a6ac2e · outbound

This paper cites Methods of conjugate gradients for solving linear systems.Journal of research of the National Bureau of Standards, 49(6):409–436, 1952.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Methods of conjugate gradients for solving linear systems.Journal of research of the National Bureau of Standards, 49(6):409–436, 1952

Reference 74

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef4648b6-41a3-46da-8d2e-b9292b759f08 · outbound

This paper cites SIAM, 2003.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards SIAM, 2003

Reference 75

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f246ce2d-75ae-44a4-b0d5-9da9de767a50 · outbound

This paper cites Springer, 4 edition, 2020.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Springer, 4 edition, 2020

Reference 76

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ae2cb35-29a7-43c5-9805-e18c08913990 · outbound

This paper cites Approximate riemann solvers, parameter vectors, and difference schemes.Journal of computational physics, 43(2):357–372, 1981.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Approximate riemann solvers, parameter vectors, and difference schemes.Journal of computational physics, 43(2):357–372, 1981

Reference 77

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb47f0e1-c1c6-4c64-b552-c5e5c29897be · outbound

This paper cites Information theory and statistical mechanics.Physical review, 106(4):620, 1957.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Information theory and statistical mechanics.Physical review, 106(4):620, 1957

Reference 78

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48dbb034-9c96-4bb9-b7f8-c22117313ee6 · outbound

This paper cites Academic Press, 11 edition, 2014.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Academic Press, 11 edition, 2014

Reference 79

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55b2cfab-85c7-4f2e-ac09-efe89111e1e0 · outbound

This paper cites Equation of state calculations by fast computing machines.The journal of chemical physics, 21(6):1087–1092, 1953.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Equation of state calculations by fast computing machines.The journal of chemical physics, 21(6):1087–1092, 1953

Reference 80

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8877ab60-e240-4a88-82e8-1fbcb8052472 · outbound

This paper cites Ziebart, Andrew Maas, J.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Ziebart, Andrew Maas, J

Reference 81

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cf82fa4-923f-4b76-b026-9384fe8b1903 · outbound

This paper cites Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 82

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d26fc3b-1ddb-4815-8dc5-f8858144caac · outbound

This paper cites Where applicable, we additionally require that solvers within a scheme family agree on smooth initial data to within their formal order at fixed resolution.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Where applicable, we additionally require that solvers within a scheme family agree on smooth initial data to within their formal order at fixed resolution

Reference 83

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 499fcfd1-cbfd-4bbf-99c1-f1e06c0457ae · outbound

This paper cites This producesabsoluteerror against the exact ue at each grid level rather than a self-consistency ratio, and detects sign errors and stencil bugs that self- convergence cannot.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards This producesabsoluteerror against the exact ue at each grid level rather than a self-consistency ratio, and detects sign errors and stencil bugs that self- convergence cannot

Reference 84

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c849d7e5-efa8-43c8-9412-8a447c9a0809 · outbound

This paper cites This provides a check against an independent benchmark, complementing self-convergence and MMS.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards This provides a check against an independent benchmark, complementing self-convergence and MMS

Reference 85

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

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