Paper Citation Record · LEDGER

Reward Model Ensembles Help Mitigate Overoptimization

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 31 inbound Pith citation observations for arXiv:2310.02743.

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

pith.paper-citation-record.v1
2310.02743 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 31 of 31 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:57:54.254039Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

2
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 56747fdd-9082-453d-8616-4d7b19198df2 · inbound

InfAlign: Inference-aware language model alignment cites this paper.

InfAlign: Inference-aware language model alignment Reward Model Ensembles Help Mitigate Overoptimization

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T23:57:54.254039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3404ba26-329b-4d9c-837a-3c7f7aaaffc3 · inbound

Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment cites this paper.

Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment Reward Model Ensembles Help Mitigate Overoptimization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T19:55:50.559707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a093c96a-6bff-4f54-83cc-876ca28f9af7 · inbound

Debate Helps Weak-to-Strong Generalization cites this paper.

Debate Helps Weak-to-Strong Generalization Reward Model Ensembles Help Mitigate Overoptimization

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T17:50:56.339928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e538dc2d-97f4-4c23-8dd9-ab195e63b329 · inbound

Reviving The Classics: Active Reward Modeling in Large Language Model Alignment cites this paper.

Reviving The Classics: Active Reward Modeling in Large Language Model Alignment Reward Model Ensembles Help Mitigate Overoptimization

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T11:47:17.522690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5258d64d-76db-48d4-accb-5a59a590a8eb · inbound

Reusing Embeddings: Reproducible Reward Model Research in Large Language Model Alignment without GPUs cites this paper.

Reusing Embeddings: Reproducible Reward Model Research in Large Language Model Alignment without GPUs Reward Model Ensembles Help Mitigate Overoptimization

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-09T11:32:47.837706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c3fe5c1-4a8a-434b-865d-c3999a37b8c8 · inbound

Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning cites this paper.

Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning Reward Model Ensembles Help Mitigate Overoptimization

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-08T14:27:51.051139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 726b7ead-80f1-4df5-a9f2-480b78a979cd · inbound

RewardAnything: Generalizable Principle-Following Reward Models cites this paper.

RewardAnything: Generalizable Principle-Following Reward Models Reward Model Ensembles Help Mitigate Overoptimization

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.902238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1560d3f1-fc0a-4acf-8370-8e9b79abaaec · inbound

Bradley-Terry and Multi-Objective Reward Modeling Are Complementary cites this paper.

Bradley-Terry and Multi-Objective Reward Modeling Are Complementary Reward Model Ensembles Help Mitigate Overoptimization

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T18:51:30.200216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12d4acc6-c2fe-47e3-8ed9-3c0a9e4d5f3e · inbound

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities cites this paper.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Reward Model Ensembles Help Mitigate Overoptimization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:24.968234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d81661bf-1992-4d60-91f9-a40889259d1e · inbound

Towards Reliable, Uncertainty-Aware Alignment cites this paper.

Towards Reliable, Uncertainty-Aware Alignment Reward Model Ensembles Help Mitigate Overoptimization

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T15:40:13.721341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32ede528-99af-4867-8c7c-71b624daa0fc · inbound

Factored Causal Representation Learning for Robust Reward Modeling in RLHF cites this paper.

Factored Causal Representation Learning for Robust Reward Modeling in RLHF Reward Model Ensembles Help Mitigate Overoptimization

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-21T14:20:13.627586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b7eced65-43f1-4476-b2e8-5780b93e53dc · inbound

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution cites this paper.

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution Reward Model Ensembles Help Mitigate Overoptimization

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-16T06:37:28.509853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7146a6c9-53c9-47d7-a33f-47bb6137bb49 · inbound

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution cites this paper.

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution Reward Model Ensembles Help Mitigate Overoptimization

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-21T13:10:10.523375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 96982241-62cb-40e8-b1b3-331ca867a0ba · inbound

Reinforcement Learning via Value Gradient Flow cites this paper.

Reinforcement Learning via Value Gradient Flow Reward Model Ensembles Help Mitigate Overoptimization

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:20:25.590488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4c002d6f-dab3-463b-8179-4bf45742812e · inbound

FUSE: Ensembling Verifiers with Zero Labeled Data cites this paper.

FUSE: Ensembling Verifiers with Zero Labeled Data Reward Model Ensembles Help Mitigate Overoptimization

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:26:09.036855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 21372565-3424-40f8-a0fe-139255f3463c · inbound

Theoretical Limits of Language Model Alignment cites this paper.

Theoretical Limits of Language Model Alignment Reward Model Ensembles Help Mitigate Overoptimization

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:30:56.966652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 48120300-4e5a-45c6-b2a4-d5056048f549 · inbound

Quantifying the Utility of User Simulators for Building Collaborative LLM Assistants cites this paper.

Quantifying the Utility of User Simulators for Building Collaborative LLM Assistants Reward Model Ensembles Help Mitigate Overoptimization

Reference 94

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:36:46.897334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5ed0fe47-9729-45d5-b6c2-bebe997b37a6 · inbound

TMPO: Trajectory Matching Policy Optimization for Diverse and Efficient Diffusion Alignment cites this paper.

TMPO: Trajectory Matching Policy Optimization for Diverse and Efficient Diffusion Alignment Reward Model Ensembles Help Mitigate Overoptimization

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:37:29.192797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 44d10863-751c-4a77-bdd7-f20068c52e47 · inbound

TMPO: Trajectory Matching Policy Optimization for Diverse and Efficient Diffusion Alignment cites this paper.

TMPO: Trajectory Matching Policy Optimization for Diverse and Efficient Diffusion Alignment Reward Model Ensembles Help Mitigate Overoptimization

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:03:02.908788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6e2d1bdb-71b1-4184-acae-d07b2895a53b · inbound

Variance-aware Reward Modeling with Anchor Guidance cites this paper.

Variance-aware Reward Modeling with Anchor Guidance Reward Model Ensembles Help Mitigate Overoptimization

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T05:12:17.505258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cbef8efc-0e05-4646-a69f-dc02226ae345 · inbound

Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs cites this paper.

Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs Reward Model Ensembles Help Mitigate Overoptimization

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-22T09:41:22.253533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f2ebf4ad-b424-4b6e-a21d-1fed4734bd6e · inbound

Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs cites this paper.

Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs Reward Model Ensembles Help Mitigate Overoptimization

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-05-22T09:41:21.386002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c0c514f6-1221-492d-82a9-150c6bb749d0 · inbound

Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs cites this paper.

Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs Reward Model Ensembles Help Mitigate Overoptimization

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-06-30T17:34:58.057414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 926e9c09-7e2b-4af4-b7dc-b6714b222c07 · inbound

Proxy Reward Internalization and Mechanistic Exploitation: A Learned Precursor to Reward Hacking and Its Generalization cites this paper.

Proxy Reward Internalization and Mechanistic Exploitation: A Learned Precursor to Reward Hacking and Its Generalization Reward Model Ensembles Help Mitigate Overoptimization

Reference 76

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T01:37:30.389148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ef72fedb-2c99-414b-baf1-f5c5b96e8386 · inbound

Uncertainty-Aware Reward Modeling for Stable RLHF cites this paper.

Uncertainty-Aware Reward Modeling for Stable RLHF Reward Model Ensembles Help Mitigate Overoptimization

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:19:30.702713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d6cad619-e42a-43b6-bd45-7ad7a0fa5bd8 · inbound

Against Proxy Optimization cites this paper.

Against Proxy Optimization Reward Model Ensembles Help Mitigate Overoptimization

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T10:49:46.643173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d9ac9227-cc96-4ce7-8e35-5affe4476085 · inbound

Designing Reward Signals for Portable Query Generation: A Case Study in Industrial Semantic Job Search cites this paper.

Designing Reward Signals for Portable Query Generation: A Case Study in Industrial Semantic Job Search Reward Model Ensembles Help Mitigate Overoptimization

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-04T13:29:51.705362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 325d1168-d703-44be-b697-bc9e1cf32054 · inbound

STEAM: Self-Supervised Temporal Ensemble Advantage Modeling for Real-World Robot Learning cites this paper.

STEAM: Self-Supervised Temporal Ensemble Advantage Modeling for Real-World Robot Learning Reward Model Ensembles Help Mitigate Overoptimization

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:24:19.479860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 50ba71a1-39ee-4081-94da-de8cc3d99fab · inbound

Multi-Turn On-Policy Distillation with Prefix Replay cites this paper.

Multi-Turn On-Policy Distillation with Prefix Replay Reward Model Ensembles Help Mitigate Overoptimization

Reference 283

Resolution: unresolved; no resolver link, observed 2026-07-11T13:53:36.775836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d41dcab2-c667-4af5-a4dc-773fd6247303 · inbound

Multi-Turn On-Policy Distillation with Prefix Replay cites this paper.

Multi-Turn On-Policy Distillation with Prefix Replay Reward Model Ensembles Help Mitigate Overoptimization

Reference 284

Resolution: unresolved; no resolver link, observed 2026-08-02T08:41:05.531058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e426a20c-f573-4c4b-a2db-4c6fccabcff7 · inbound

More Convincing, Not More Correct: Self-Play Reward Hacking of Reference-Free LLM Judges cites this paper.

More Convincing, Not More Correct: Self-Play Reward Hacking of Reference-Free LLM Judges Reward Model Ensembles Help Mitigate Overoptimization

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-07-08T21:25:38.636176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.