Paper Citation Record · LEDGER

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning

As of 8 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2608.01837.

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

pith.paper-citation-record.v1
2608.01837 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:59:00.345669Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

23 of 23 outbound references displayed

verified exact0
verified fuzzy0
unresolved23
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6b017c0d-f03d-4ccb-8932-104c8caa8a47 · outbound

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

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.219331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9af2218-9e07-49e4-892c-592fd98de37b · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.227017Z

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Observation 7cb6a7a2-acd4-4b33-ba4b-13c49ecc31be · outbound

This paper cites Jin, B.; Zeng, H.; Yue, Z.; Yoon, J.; Arik, S.; Wang, D.; Zamani, H.; and Han, J.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning Jin, B.; Zeng, H.; Yue, Z.; Yoon, J.; Arik, S.; Wang, D.; Zamani, H.; and Han, J

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.239132Z

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Observation c151a5c0-e625-44b6-983b-3893a5ec2401 · outbound

This paper cites Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.244103Z

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Observation 9ad32867-622b-4d20-b0d3-4c43daf4cd8a · outbound

This paper cites DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.250021Z

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Observation 87a09415-9b73-4196-9249-06d9fd5abf17 · outbound

This paper cites PhysAgent: Automating Physics-Based 4D Synthesis via Trajectory-Grounded Multi-Agent Feedback.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning PhysAgent: Automating Physics-Based 4D Synthesis via Trajectory-Grounded Multi-Agent Feedback

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.257672Z

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Observation 4320ecfa-9e30-413f-87b0-d8c6f7065087 · outbound

This paper cites InInternational Conference on Learning Representations, volume 2025, 79791–79821.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning InInternational Conference on Learning Representations, volume 2025, 79791–79821

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.262943Z

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Observation 360a4500-963f-4a8b-8e26-ab48d2e3b0d2 · outbound

This paper cites InInternational Conference on Learning Representations, volume 2025, 406–441.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning InInternational Conference on Learning Representations, volume 2025, 406–441

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.269190Z

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Observation eb569ea2-68d2-473b-9230-e9ff688e6a94 · outbound

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

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.281220Z

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Observation f5e41371-9c31-437b-8c28-1f4276d127cf · outbound

This paper cites OpenAI GPT-5 System Card.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning OpenAI GPT-5 System Card

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.295295Z

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Observation f0ae667c-bd3b-4388-85ab-f70d5e94d9c3 · outbound

This paper cites Wu, J.; Yang, S.; Lu, Z.; Zhang, F.; Shen, Y.; Feng, L.; Luo, H.; Lian, Z.; Zhang, S.; Wen, Z.; et al.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning Wu, J.; Yang, S.; Lu, Z.; Zhang, F.; Shen, Y.; Feng, L.; Luo, H.; Lian, Z.; Zhang, S.; Wen, Z.; et al

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.302769Z

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Observation ae6bd321-e27e-454b-8cf6-342a6b55124e · outbound

This paper cites SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.309548Z

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Observation 0dd09122-7844-423c-aa64-af12f6a62a2b · outbound

This paper cites $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.315255Z

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Observation e9a70feb-d516-40b4-a4bc-04fc0db54f72 · outbound

This paper cites On-Policy Context Distillation for Language Models.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning On-Policy Context Distillation for Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.321172Z

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Observation cce752fe-1a0a-4975-9c80-710fe81be3db · outbound

This paper cites GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.326323Z

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Observation 05c7914f-cb14-4254-a500-48cadc48c2bf · outbound

This paper cites StepOPSD: Step-Aware Online Preference Distillation for Agent Reinforcement Learning.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning StepOPSD: Step-Aware Online Preference Distillation for Agent Reinforcement Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.333551Z

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Observation fc182e4b-37fb-4bd3-946e-c13e12c1800f · outbound

This paper cites Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.340360Z

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Observation b79249ad-8e10-426f-b83a-aaa012a91ea5 · outbound

This paper cites SOD: Step-wise On-policy Distillation for Small Language Model Agents.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning SOD: Step-wise On-policy Distillation for Small Language Model Agents

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.345669Z

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Observation 92065c53-55e6-4066-8cbb-b3939c8ddb52 · outbound

This paper cites Proximal Policy Optimization Algorithms.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.275824Z

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Observation 3041e6d0-9ab0-4bea-8acd-83e95392c18d · outbound

This paper cites ALFWorld: Aligning Text and Embodied Environments for Interactive Learning.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.287378Z

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Observation 509ab239-d545-477e-8445-3c7f3fd7cc1a · outbound

This paper cites Comanici, G.; Bieber, E.; Schaekermann, M.; Pasupat, I.; Sachdeva, N.; Dhillon, I.; Blistein, M.; Ram, O.; Zhang, D.; Rosen, E.; et al.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning Comanici, G.; Bieber, E.; Schaekermann, M.; Pasupat, I.; Sachdeva, N.; Dhillon, I.; Blistein, M.; Ram, O.; Zhang, D.; Rosen, E.; et al

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.205690Z

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Observation 17be89a9-46cb-4218-8e82-85cde710ba52 · outbound

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

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.212946Z

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Observation 702c1795-5fd7-4e5e-b1b0-0791da9e6b4c · outbound

This paper cites Reinforcement Learning via Self-Distillation.

PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning Reinforcement Learning via Self-Distillation

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:00.232410Z

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Pith citing papers

No inbound Pith citation observations are available.