Paper Citation Record · LEDGER

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment

As of 20 August 2026, this Paper Citation Record lists 87 of 87 outbound references and 4 inbound Pith citation observations for arXiv:2412.14516.

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

pith.paper-citation-record.v1
2412.14516 v1

Coverage vector

measured 87 of 87 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:16:32.070018Z

measured 91 of 91 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:58:38.854695Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T04:42:04.899091Z

Reference resolution

87 of 87 outbound references displayed

verified exact1
verified fuzzy30
unresolved56
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a3043f17-7c1a-4907-9627-c1f5b1960ec7 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.787908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a53bf06-8323-4cfd-805a-41592a79ce75 · outbound

This paper cites Training language models to follow instructions with human feedback.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Training language models to follow instructions with human feedback

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.792312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1c5331e-62a6-4b14-8f7e-9d06a1242baf · outbound

This paper cites Learning to summarize with human feedback.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Learning to summarize with human feedback

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.795485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c21db7c7-e901-4ace-aa9d-f03d596f7616 · outbound

This paper cites Deep reinforcement learning from human preferences.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Deep reinforcement learning from human preferences

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.798253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ab6c605-1107-4c47-ba23-174ba61bb750 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Proximal Policy Optimization Algorithms

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.800935Z

Source-reported events for the cited work

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Observation c2e03700-8331-4b9a-9237-0832dace122f · outbound

This paper cites Implementation matters in deep policy gradients: A case study on ppo and trpo.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Implementation matters in deep policy gradients: A case study on ppo and trpo

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.805323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8364072-296c-4bfa-9f37-7b5b687dd2c0 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Direct preference optimization: Your language model is secretly a reward model

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.809910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de430066-de2d-42a1-aab7-fe0e94031552 · outbound

This paper cites A general theoretical paradigm to understand learning from human preferences.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment A general theoretical paradigm to understand learning from human preferences

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.813699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 252bde81-682b-48cd-b034-cb66fe3413f1 · outbound

This paper cites SLiC-HF: Sequence Likelihood Calibration with Human Feedback.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment SLiC-HF: Sequence Likelihood Calibration with Human Feedback

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.816513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1260564c-4074-4593-9df5-ec7ae10168ed · outbound

This paper cites Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.820225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d69ef4d6-85ad-494f-a7e1-c9a4554f450f · outbound

This paper cites Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.823443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99b3e0ae-f37d-4861-986f-cdd089439c71 · outbound

This paper cites Advancing LLM Reasoning Generalists with Preference Trees.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Advancing LLM Reasoning Generalists with Preference Trees

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.827786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6796dcc0-2ce7-4394-805f-483abf14982d · outbound

This paper cites Learning word vectors for sentiment analysis.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Learning word vectors for sentiment analysis

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.831179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cd60717-3a3a-459c-8c01-687001bbea5c · outbound

This paper cites Tl; dr: Mining reddit to learn automatic summarization.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Tl; dr: Mining reddit to learn automatic summarization

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.711917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation edadd08e-1c6a-43ed-998e-18509afea875 · outbound

This paper cites A framework for few-shot language model evaluation, 12 2023.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment A framework for few-shot language model evaluation, 12 2023

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.838747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65c40c16-36fe-473b-ad35-f090bd37ad5b · outbound

This paper cites Nash Learning from Human Feedback.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Nash Learning from Human Feedback

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.841413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec0e0994-acd2-4e42-a38b-9f8c176824b2 · outbound

This paper cites Statistical rejection sampling improves preference optimization.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Statistical rejection sampling improves preference optimization

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.700617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 70cce962-625c-40dd-ad7e-be59950a25ca · outbound

This paper cites Self-Rewarding Language Models.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Self-Rewarding Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.847944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0762ebe0-d00d-443c-ba4f-58f67230247e · outbound

This paper cites Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.851281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6819dafb-6643-4018-b926-430f6136ac17 · outbound

This paper cites Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.855210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 087d7327-30ac-4fe0-9c68-53e750be321b · outbound

This paper cites Direct Language Model Alignment from Online AI Feedback.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Direct Language Model Alignment from Online AI Feedback

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.859337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7690f04-8146-4675-90d9-319bb5331e75 · outbound

This paper cites Simper: Simple preference fine-tuning without hyperparameters by perplexity optimization.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Simper: Simple preference fine-tuning without hyperparameters by perplexity optimization

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.693022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 07476bde-f31a-476d-8daa-12c33e203891 · outbound

This paper cites Policy Optimization in RLHF: The Impact of Out-of-preference Data.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Policy Optimization in RLHF: The Impact of Out-of-preference Data

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.865955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3207bb2-2819-4a05-88bf-8338026d52c2 · outbound

This paper cites Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.868865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00fe9418-3368-48a4-9310-8b5c3d35de57 · outbound

This paper cites Provably Robust DPO: Aligning Language Models with Noisy Feedback.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Provably Robust DPO: Aligning Language Models with Noisy Feedback

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.872076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c89ca9c3-a17a-4814-871d-11c438522a3a · outbound

This paper cites Noise Contrastive Alignment of Language Models with Explicit Rewards.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Noise Contrastive Alignment of Language Models with Explicit Rewards

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.875936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4960576a-471c-4581-801a-9b1107e24467 · outbound

This paper cites COPR: Continual Human Preference Learning via Optimal Policy Regularization.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment COPR: Continual Human Preference Learning via Optimal Policy Regularization

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-11T12:16:32.256071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c4044b74-173e-4ab9-8a56-2590f91da535 · outbound

This paper cites Towards Efficient Exact Optimization of Language Model Alignment.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Towards Efficient Exact Optimization of Language Model Alignment

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.882201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 793ac3b3-1a83-4aea-9f02-c5230f473f25 · outbound

This paper cites Noise contrastive estimation and negative sampling for condi- tional models: Consistency and statistical efficiency.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Noise contrastive estimation and negative sampling for condi- tional models: Consistency and statistical efficiency

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.684069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ea2b61fe-61ac-47a8-9026-a9ad5900318c · outbound

This paper cites Towards Analyzing and Understanding the Limitations of DPO: A Theoretical Perspective.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Towards Analyzing and Understanding the Limitations of DPO: A Theoretical Perspective

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.888937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53babd5a-45e5-4f9d-8b08-949cb10d1c75 · outbound

This paper cites From $r$ to $Q^*$: Your Language Model is Secretly a Q-Function.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment From $r$ to $Q^*$: Your Language Model is Secretly a Q-Function

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.891745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4659949-2b58-4b5b-8532-ade787f3a012 · outbound

This paper cites DPO Meets PPO: Reinforced Token Optimization for RLHF.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment DPO Meets PPO: Reinforced Token Optimization for RLHF

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.895081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df554fa1-4c4e-4e47-8839-3d33f48fea7a · outbound

This paper cites Token-level Direct Preference Optimization.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Token-level Direct Preference Optimization

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.898358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1343b00a-f14a-4d03-ae9d-80f0692c6f1a · outbound

This paper cites A general offline reinforcement learning framework for interac- tive recommendation.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment A general offline reinforcement learning framework for interac- tive recommendation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.674683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6f6fb95b-bc85-4058-9a5f-7997b2df7c71 · outbound

This paper cites On calibration of modern neural networks.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment On calibration of modern neural networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.664634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 64781a79-dc7c-4d9e-82a9-962a674930b6 · outbound

This paper cites Scale calibration of deep ranking models.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Scale calibration of deep ranking models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.655401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dfdcb30e-710b-4eb5-9e07-495b1f6bcc7d · outbound

This paper cites Calibrated model-based deep reinforcement learning.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Calibrated model-based deep reinforcement learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.646066Z

Source-reported events for the cited work

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Observation 5714eafd-90fb-415e-b599-e941e60eb9b0 · outbound

This paper cites Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.916373Z

Source-reported events for the cited work

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Observation 8437b2c8-1af5-4ddd-9ab1-d93fa3439987 · outbound

This paper cites On the Calibration of Large Language Models and Alignment.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment On the Calibration of Large Language Models and Alignment

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.919947Z

Source-reported events for the cited work

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Observation 9312365d-b111-474d-b068-e16fa8b1bee6 · outbound

This paper cites Language Models (Mostly) Know What They Know.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Language Models (Mostly) Know What They Know

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.922990Z

Source-reported events for the cited work

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Observation 56d76232-f930-4dd0-af5b-52442956b335 · outbound

This paper cites Rank analysis of incomplete block designs: I.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Rank analysis of incomplete block designs: I

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.631213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 83463c92-b4e6-4ccd-8540-2d4bb110ed37 · outbound

This paper cites Generalized Preference Optimization: A Unified Approach to Offline Alignment.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Generalized Preference Optimization: A Unified Approach to Offline Alignment

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.930130Z

Source-reported events for the cited work

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Observation a4256de8-453d-45da-95c8-e554d4f05985 · outbound

This paper cites How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.933188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0943c1fb-e0db-48ed-9cf5-9f167b4771ef · outbound

This paper cites SimPO: Simple Preference Optimization with a Reference-Free Reward.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment SimPO: Simple Preference Optimization with a Reference-Free Reward

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.937213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac1a2811-daad-4d69-bb65-a0193be5fac8 · outbound

This paper cites Reinforced Self-Training (ReST) for Language Modeling.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Reinforced Self-Training (ReST) for Language Modeling

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.940300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25e9db84-767a-48ea-b70d-e03cd68fe5a9 · outbound

This paper cites Openchat: Advancing open-source language models with mixed-quality data.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Openchat: Advancing open-source language models with mixed-quality data

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.621160Z

Source-reported events for the cited work

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Observation 84b5bfc4-f444-45dd-ac05-f508bab73996 · outbound

This paper cites Fine-Tuning Language Models with Advantage-Induced Policy Alignment.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Fine-Tuning Language Models with Advantage-Induced Policy Alignment

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.947063Z

Source-reported events for the cited work

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Observation fa582487-b8bf-4e08-9c55-7a73db68d3b8 · outbound

This paper cites Machine learning: a probabilistic perspective.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Machine learning: a probabilistic perspective

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.949861Z

Source-reported events for the cited work

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Observation 42d83879-5ccc-4995-84eb-62af187d97d4 · outbound

This paper cites Improving Policy Gradient by Exploring Under-appreciated Rewards.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Improving Policy Gradient by Exploring Under-appreciated Rewards

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.952713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a03ab81-e9a4-4ed8-a15e-156dba954253 · outbound

This paper cites Learning how to propagate messages in graph neural networks.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Learning how to propagate messages in graph neural networks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.602814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 00ecfb45-e50e-47c1-b8fa-c9e7af06eb87 · outbound

This paper cites Learning to generalize from sparse and underspecified rewards.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Learning to generalize from sparse and underspecified rewards

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.591645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f71633dc-4925-4aad-8ec1-3b59b4422e85 · outbound

This paper cites Decoupled self-supervised learning for graphs.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Decoupled self-supervised learning for graphs

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.581460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f57ea6f7-d3af-4c71-b8aa-df3d3e2ac0ef · outbound

This paper cites UltraFeedback: Boosting Language Models with Scaled AI Feedback.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment UltraFeedback: Boosting Language Models with Scaled AI Feedback

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.964333Z

Source-reported events for the cited work

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Observation 5a888593-3615-4760-a93b-621af2650ef9 · outbound

This paper cites Zephyr: Direct Distillation of LM Alignment.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Zephyr: Direct Distillation of LM Alignment

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.967147Z

Source-reported events for the cited work

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Observation 8842ab19-1cfe-4140-910d-d92946430a09 · outbound

This paper cites MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.970515Z

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Observation 8a528a66-0fca-47d9-91f4-689ff083e0ae · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.973598Z

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Observation 53e1e8af-cde6-4422-ac5d-4e030c0db960 · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Instruction-Following Evaluation for Large Language Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.977059Z

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Observation 38d0ece0-5b9d-4acd-89ef-12140dc30d40 · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.980152Z

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Observation 9f242457-b2f7-4380-9e7b-3bcb877d3602 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.983210Z

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Observation 085c13ee-d299-423b-b2c1-408a20437e10 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Training Verifiers to Solve Math Word Problems

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.986326Z

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Observation f68d5328-0d1d-41ed-86d2-480ad6135bdf · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Measuring Mathematical Problem Solving With the MATH Dataset

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.989194Z

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Observation 959e2286-93db-4f1d-bf5b-cc7288c8181c · outbound

This paper cites Alpacaeval: An automatic evaluator of instruction-following models, 2023.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Alpacaeval: An automatic evaluator of instruction-following models, 2023

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.991750Z

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Observation b5c75b4a-1225-4dd4-bbf8-58a1b9a2280f · outbound

This paper cites Pythia: A suite for analyzing large language models across training and scaling.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Pythia: A suite for analyzing large language models across training and scaling

Reference 63

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Observation 3eecb998-3173-402e-b8d0-ea74583ca919 · outbound

This paper cites Language models are unsupervised multitask learners.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Language models are unsupervised multitask learners

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.559450Z

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Observation d2f9f718-aa82-40fd-96c9-61af13d31330 · outbound

This paper cites Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.999493Z

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Observation dc65e0f0-fbbf-4f39-a91c-6616e4399414 · outbound

This paper cites Beyond reverse kl: Generalizing direct preference optimization with diverse divergence constraints.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Beyond reverse kl: Generalizing direct preference optimization with diverse divergence constraints

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.549991Z

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Observation 650830fe-ea77-4a4c-b4dd-a0dc74095e6d · outbound

This paper cites Iterative Reasoning Preference Optimization.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Iterative Reasoning Preference Optimization

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:32.006296Z

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Observation 33bd544e-d5b4-4168-b0a0-581e3d3d6058 · outbound

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Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Information, divergence and risk for binary experiments

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.540757Z

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Observation da37e0fa-1e0f-437f-9935-12465a4d6df0 · outbound

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Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Reward augmented maximum likelihood for neural structured prediction

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.530302Z

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Observation 05b24dda-9261-44f0-9a92-abeb00905921 · outbound

This paper cites Clustering with bregman divergences.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Clustering with bregman divergences

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.520990Z

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Observation 41168029-a0b7-4045-82c1-31903ff9efa5 · outbound

This paper cites Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:32.019439Z

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Observation 484e4191-a869-4b78-bbf9-af172e8cac7c · outbound

This paper cites Understanding the Effects of RLHF on LLM Generalisation and Diversity.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Understanding the Effects of RLHF on LLM Generalisation and Diversity

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:32.022520Z

Source-reported events for the cited work

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Observation 8210680e-89c7-418e-bff8-ce1a1fcc093d · outbound

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Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.511971Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 26608078-d248-42dd-abf9-23a4eb8d7b11 · outbound

This paper cites Limitations.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Limitations

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.501635Z

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Observation 4884cd23-33a9-4595-8b34-3bae026efaf8 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include theoretical results.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.489123Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4826bd82-96f4-4751-875e-7cee43bda8b5 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Guidelines: • The answer NA means that the paper does not include experiments

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.476708Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c0db1cac-3a60-4de3-8d6a-1daad2ad7748 · outbound

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Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment • Please see the NeurIPS code and data submission guidelines ( https://nips.cc/ public/guides/CodeSubmissionPolicy) for more details

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.467290Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation aec86a50-2589-4464-aa65-e396144e2d65 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Guidelines: • The answer NA means that the paper does not include experiments

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.455381Z

Source-reported events for the cited work

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Observation 06642fd1-12b3-4dcd-bb92-f67df389f24a · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Guidelines: • The answer NA means that the paper does not include experiments

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.446467Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 28738ff5-4694-48a8-8d58-87fd63ab9128 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Guidelines: • The answer NA means that the paper does not include experiments

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.437364Z

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Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.426109Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f9cea911-8a11-41e2-917e-369700efd3ee · outbound

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Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.414192Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9bb2e117-02d4-49b7-b2ec-819966320e54 · outbound

This paper cites Guidelines: • The answer NA means that the paper poses no such risks.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Guidelines: • The answer NA means that the paper poses no such risks

Reference 83

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Observation ea19d25d-37db-4e40-9eea-312ff696fdf3 · outbound

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Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Guidelines: • The answer NA means that the paper does not use existing assets

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.397363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 76ebee73-8f0e-4919-9cc8-415d8e5c5bb6 · outbound

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Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Guidelines: • The answer NA means that the paper does not release new assets

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:32.064064Z

Source-reported events for the cited work

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Observation 5df0804e-650e-45aa-91b6-34563bce2854 · outbound

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Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:32.067064Z

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Unavailable: canonical work link unavailable.

Observation ea1687f3-4a6e-4d5d-aeef-083d0bd918a7 · outbound

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Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:16:32.375743Z

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Pith citing papers

Observation 5983e2f6-f1c3-49aa-999c-9598cfd3d96f · inbound

DPO-Shift: Shifting the Distribution of Direct Preference Optimization cites this paper.

DPO-Shift: Shifting the Distribution of Direct Preference Optimization Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment

Reference 34

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Observation b2ecad65-726b-42cc-a8d2-a16489530897 · inbound

Policy-labeled Preference Learning: Is Preference Enough for RLHF? cites this paper.

Policy-labeled Preference Learning: Is Preference Enough for RLHF? Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T23:58:38.854695Z

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Observation 4b664141-2b19-4be9-bc82-8fb1de2a8e68 · inbound

Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems cites this paper.

Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment

Reference 150

Resolution: verified exact; arxiv_id, observed 2026-05-19T04:42:04.901351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 10e735fa-5805-42b3-a9da-57eff2c8f526 · inbound

Gradient-Gated DPO: Stabilizing Preference Optimization in Language Models cites this paper.

Gradient-Gated DPO: Stabilizing Preference Optimization in Language Models Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:20:41.691864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.