Paper Citation Record · LEDGER

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

As of 25 July 2026, this Paper Citation Record lists 31 of 31 outbound references and 16 inbound Pith citation observations for arXiv:2510.18471.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2510.18471 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-18T05:16:28.008746Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-25T06:30:59.84592+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T14:30:20.959431Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-10T12:15:01.137692Z

Reference resolution

31 of 31 outbound references displayed

verified exact24
verified fuzzy4
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cb50c0e3-7c6a-4401-9a99-147dea01ef50 · outbound

This paper cites OpenCodeReasoning: Advancing Data Distillation for Competitive Coding.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment OpenCodeReasoning: Advancing Data Distillation for Competitive Coding

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:20:54.609566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 133023d3-99be-4eae-9140-1fb305078c90 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment Evaluating Large Language Models Trained on Code

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:20:54.602730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 35c381a8-345a-4eea-b64f-bc280f5d75ff · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:20:54.624448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 9f1ccf0c-4e9f-499f-85d0-45291f2e4ac4 · outbound

This paper cites Process Reinforcement through Implicit Rewards.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment Process Reinforcement through Implicit Rewards

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:20:54.579753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 19022fa2-d24f-4201-97be-b9d8355ac07a · outbound

This paper cites CodeScore: Evaluating Code Generation by Learning Code Execution.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment CodeScore: Evaluating Code Generation by Learning Code Execution

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T05:20:54.620410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation f3ba0fca-58f2-4a83-98fe-1e1ceb21b23b · outbound

This paper cites A Survey on Code Generation with LLM-based Agents.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment A Survey on Code Generation with LLM-based Agents

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-19T23:02:21.366092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 33cd7339-6bfe-4775-ac0e-7e589667d67e · outbound

This paper cites ReCode: Reinforcing Code Generation with Reasoning-Process Rewards.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment ReCode: Reinforcing Code Generation with Reasoning-Process Rewards

Reference 7

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T05:20:54.545492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation a92c6490-3bd4-4fd2-9249-c8876bf892ad · outbound

This paper cites MiniLLM: On-Policy Distillation of Large Language Models.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment MiniLLM: On-Policy Distillation of Large Language Models

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:20:54.589881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 42fdce16-fd98-40e5-8748-dc2aaa83dbe6 · outbound

This paper cites The False Promise of Imitating Proprietary LLMs.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment The False Promise of Imitating Proprietary LLMs

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-18T06:54:31.902059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 86d251eb-e19e-42fc-aca2-e7b41d675a63 · outbound

This paper cites Teaching Large Language Models to Reason with Reinforcement Learning.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment Teaching Large Language Models to Reason with Reinforcement Learning

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:20:54.606098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 4509fc09-955f-49be-9835-cd4b484f275c · outbound

This paper cites Skywork Open Reasoner 1 Technical Report.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment Skywork Open Reasoner 1 Technical Report

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:20:54.576707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation c92de9ca-b71f-443b-a17b-025fe33c0ba0 · outbound

This paper cites Measuring Coding Challenge Competence With APPS.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment Measuring Coding Challenge Competence With APPS

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:20:54.613065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation bc0ebe0d-b6de-4509-8776-3e848d148cc1 · outbound

This paper cites Designing and interpreting probes with control tasks.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment Designing and interpreting probes with control tasks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:25:55.141010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation feda402c-46a4-464d-9774-51e523e764a1 · outbound

This paper cites Qwen2.5-Coder Technical Report.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment Qwen2.5-Coder Technical Report

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:20:54.522277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 428da1f5-5f6a-42a0-9113-5b1de683fc1d · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:20:54.599756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation fae20ebe-62cb-41d4-8060-b9a257df0488 · outbound

This paper cites SEED: customize large language models with sample- efficient adaptation for code generation.CoRR, abs/2403.00046, 2024a.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment SEED: customize large language models with sample- efficient adaptation for code generation.CoRR, abs/2403.00046, 2024a

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:20:54.555648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 883330c4-a3b1-4746-b00f-3c2b18ea02a0 · outbound

This paper cites How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:20:54.586746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 498c88f4-de7e-41d7-b0fb-2772d6d3bf35 · outbound

This paper cites TACO: Topics in Algorithmic COde generation dataset.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment TACO: Topics in Algorithmic COde generation dataset

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:20:54.527865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 67f68427-9c13-4e8f-8a61-4f441c4caee8 · outbound

This paper cites GPT-4 Technical Report.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment GPT-4 Technical Report

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:20:54.616387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation d75a2d4a-d82e-47e6-924a-1b12a506e19b · outbound

This paper cites Code Llama: Open Foundation Models for Code.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment Code Llama: Open Foundation Models for Code

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:20:54.631628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation f338b61f-d491-4256-adb8-294aac569a5d · outbound

This paper cites Proximal Policy Optimization Algorithms.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment Proximal Policy Optimization Algorithms

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:20:54.627938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 1ef83e66-5b18-4934-a089-3cab40d05de3 · outbound

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

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:20:54.559062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 7b23b830-ed05-4bf7-99dc-b4222ef13cee · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment HybridFlow: A Flexible and Efficient RLHF Framework

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:20:54.549662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation bf918166-6fc3-4071-8ec0-d28d694f7339 · outbound

This paper cites CodeReasoner: Enhancing the Code Reasoning Ability with Reinforcement Learning.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment CodeReasoner: Enhancing the Code Reasoning Ability with Reinforcement Learning

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:20:54.583092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 7bd90871-6fa5-4941-b7e0-192da2733377 · outbound

This paper cites CodeBoost: Boosting Code LLMs by Squeezing Knowledge from Code Snippets with RL.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment CodeBoost: Boosting Code LLMs by Squeezing Knowledge from Code Snippets with RL

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:20:54.573655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation c8a3f9db-b5ba-4130-b6c0-afbef25450ab · outbound

This paper cites Co-evolving llm coder and unit tester via reinforcement learning.arXiv preprint arXiv:2506.03136.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment Co-evolving llm coder and unit tester via reinforcement learning.arXiv preprint arXiv:2506.03136

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T05:20:54.562712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation c5509687-8d24-4de0-8b8a-917d97791d98 · outbound

This paper cites LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient Training of Code LLMs.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient Training of Code LLMs

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:20:54.593415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation d6eb99f1-bcfd-41d2-bf6a-f13127b68e17 · outbound

This paper cites A Survey on Knowledge Distillation of Large Language Models.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment A Survey on Knowledge Distillation of Large Language Models

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:20:54.566348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 835bd351-177b-4e26-abc7-1012ea600259 · outbound

This paper cites Here's a step-by-step approach to achieve this:1.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment Here's a step-by-step approach to achieve this:1

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:25:55.133531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation fb4308cd-a36e-4705-927c-258161822413 · outbound

This paper cites Initialize a Dictionary to Track Blocks: Use a dictionary to map the top-left corner of each 2x2 block to the count of black cells in that block.2.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment Initialize a Dictionary to Track Blocks: Use a dictionary to map the top-left corner of each 2x2 block to the count of black cells in that block.2

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:25:55.136519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 815fb496-5bf3-4a86-b4e5-5b659f91ef37 · outbound

This paper cites an unresolved cited work.

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment Unresolved cited work

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:25:55.138529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Pith citing papers

Observation d84a2454-7244-45eb-b109-e7f1f8a552cd · inbound

Think Anywhere in Code Generation cites this paper.

Think Anywhere in Code Generation CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-13T23:18:25.612924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 6a76c5aa-6361-4f47-8d5e-46524980184e · inbound

TestDecision: Sequential Test Suite Generation via Greedy Optimization and Reinforcement Learning cites this paper.

TestDecision: Sequential Test Suite Generation via Greedy Optimization and Reinforcement Learning CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-05-13T21:38:18.780983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 830188b8-49f9-4487-9787-53a440b74aeb · inbound

Evaluating the Formal Reasoning Capabilities of Large Language Models through Chomsky Hierarchy cites this paper.

Evaluating the Formal Reasoning Capabilities of Large Language Models through Chomsky Hierarchy CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-05-13T19:53:11.753782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation b9f4dcbc-5880-46cb-8691-fba541979f9c · inbound

SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models cites this paper.

SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-10T07:01:49.478225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation dfa64b51-5155-4abd-b316-84f35d5c18d6 · inbound

Schedule-and-Calibrate: Utility-Guided Multi-Task Reinforcement Learning for Code LLMs cites this paper.

Schedule-and-Calibrate: Utility-Guided Multi-Task Reinforcement Learning for Code LLMs CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-11T20:26:10.469171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation bb4b95a2-b255-4026-908e-42ee84ae03c4 · inbound

Boosting Reinforcement Learning with Verifiable Rewards via Randomly Selected Few-Shot Guidance cites this paper.

Boosting Reinforcement Learning with Verifiable Rewards via Randomly Selected Few-Shot Guidance CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-05-15T03:19:43.108008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 7c6a563f-8d8c-4ba8-ad5f-9cff54d5c9dc · inbound

Code as Agent Harness cites this paper.

Code as Agent Harness CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 101

Resolution: verified exact; local_arxiv, observed 2026-05-20T10:58:14.219194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation a33f4fe7-b7ed-4d90-9ab7-71c8191ca18e · inbound

Distilling Game Code World Model Generation into Lightweight Large Language Models cites this paper.

Distilling Game Code World Model Generation into Lightweight Large Language Models CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 15

Resolution: metadata mismatch; local_arxiv, observed 2026-06-30T14:04:44.660858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 737d4a5e-111d-4cbb-b10a-0c5d1446197f · inbound

Improving Small Language Models for Code Generation with Reinforcement Learning from Verification Feedback cites this paper.

Improving Small Language Models for Code Generation with Reinforcement Learning from Verification Feedback CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-06-29T14:53:32.279726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 45b56bcc-7699-4426-ac98-e248ca395c1b · inbound

TAPO: Tool-Aware Policy Optimization via Credit Transfer for Multimodal Search Agents cites this paper.

TAPO: Tool-Aware Policy Optimization via Credit Transfer for Multimodal Search Agents CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 29

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T12:16:57.774024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation aa580c9a-1676-4f23-8d9d-52405be4a5f2 · inbound

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning cites this paper.

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T21:07:23.933247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 54136184-f7a3-452a-b109-c60542fdb2ef · inbound

Attention Amnesia in Hybrid LLMs: When CoT Fine-Tuning Breaks Long-Range Recall, and How to Fix It cites this paper.

Attention Amnesia in Hybrid LLMs: When CoT Fine-Tuning Breaks Long-Range Recall, and How to Fix It CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-06-27T13:10:55.917848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 1fa12586-08a0-4488-af79-31c964d593d0 · inbound

Harnessing Routing Foresight for Micro-step-level MoE load balancing in RL Post-training cites this paper.

Harnessing Routing Foresight for Micro-step-level MoE load balancing in RL Post-training CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-07-03T12:58:08.768615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation e2839e89-9d81-4a5b-9f33-b3947090af37 · inbound

From Trainee to Trainer: LLM-Designed Training Environment for RL with Multi-Agent Reasoning cites this paper.

From Trainee to Trainer: LLM-Designed Training Environment for RL with Multi-Agent Reasoning CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-06-27T01:00:19.806171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation a9389d13-50cf-41b5-ad78-a794dfa3a7fa · inbound

When Do Intrinsic Rewards Work for Code Reasoning? A Comprehensive Study cites this paper.

When Do Intrinsic Rewards Work for Code Reasoning? A Comprehensive Study CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-07-04T04:19:33.972007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-25T06:30:59.84592+00:00.

Observation 401e2898-b4b6-464c-8117-91fcc19a8df3 · inbound

Turning Off-Policy Tokens On-Policy: A Plug-in Approach for Improving LLM Alignment cites this paper.

Turning Off-Policy Tokens On-Policy: A Plug-in Approach for Improving LLM Alignment CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 5

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