Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints | Paper Citation Record

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This paper cites A Comprehensive Survey on Evaluating Large Language Model Applications in the Medical Industry.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints A Comprehensive Survey on Evaluating Large Language Model Applications in the Medical Industry

Reference 1

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This paper cites On Large Language Models in Mission-Critical IT Governance: Are We Ready Yet?.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints On Large Language Models in Mission-Critical IT Governance: Are We Ready Yet?

Reference 2

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This paper cites Safe RLHF: Safe reinforcement learning from human feedback.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Safe RLHF: Safe reinforcement learning from human feedback

Reference 3

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This paper cites One-shot safety alignment for large language models via optimal dualization.Advances in Neural Information Processing Systems, 37:84350–84383, 2024.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints One-shot safety alignment for large language models via optimal dualization.Advances in Neural Information Processing Systems, 37:84350–84383, 2024

Reference 4

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This paper cites Stepwise alignment for constrained language model policy optimization.Advances in Neural Information Processing Systems, 37:104471–104520, 2024.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Stepwise alignment for constrained language model policy optimization.Advances in Neural Information Processing Systems, 37:104471–104520, 2024

Reference 5

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This paper cites Enhancing LLM Safety via Constrained Direct Preference Optimization.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Enhancing LLM Safety via Constrained Direct Preference Optimization

Reference 6

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This paper cites Enhancing Safety in Reinforcement Learning with Human Feedback via Rectified Policy Optimization.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Enhancing Safety in Reinforcement Learning with Human Feedback via Rectified Policy Optimization

Reference 7

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This paper cites L3Ms -- Lagrange Large Language Models.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints L3Ms -- Lagrange Large Language Models

Reference 8

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This paper cites Adversarial training for high-stakes reliability.Advances in neural information processing systems, 35: 9274–9286, 2022.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Adversarial training for high-stakes reliability.Advances in neural information processing systems, 35: 9274–9286, 2022

Reference 9

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This paper cites Confronting reward model overoptimization with constrained RLHF.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Confronting reward model overoptimization with constrained RLHF

Reference 10

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This paper cites Large lan- guage models struggle to learn long-tail knowledge.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Large lan- guage models struggle to learn long-tail knowledge

Reference 11

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This paper cites The devil is in the tails: How long-tailed code distributions impact large language models.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints The devil is in the tails: How long-tailed code distributions impact large language models

Reference 12

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Unresolved cited work

Reference 13

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This paper cites The neglected tails in vision-language models.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints The neglected tails in vision-language models

Reference 14

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This paper cites The Law of Knowledge Overshadowing: Towards Understanding, Predicting, and Preventing LLM Hallucination.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints The Law of Knowledge Overshadowing: Towards Understanding, Predicting, and Preventing LLM Hallucination

Reference 15

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This paper cites Is gpt-oss good? a comprehensive evaluation of openai’s latest open source models.arXiv preprint arXiv:2508.12461, 2025.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Is gpt-oss good? a comprehensive evaluation of openai’s latest open source models.arXiv preprint arXiv:2508.12461, 2025

Reference 16

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This paper cites Probably approximately correct constrained learning.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Probably approximately correct constrained learning

Reference 17

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This paper cites Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach

Reference 18

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This paper cites Strong duality relations in nonconvex risk- constrained learning.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Strong duality relations in nonconvex risk- constrained learning

Reference 19

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Unresolved cited work

Reference 20

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This paper cites Constrained sliced wasserstein embedding, 06 2025.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Constrained sliced wasserstein embedding, 06 2025

Reference 21

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This paper cites Near-Optimal Solutions of Constrained Learning Problems.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Near-Optimal Solutions of Constrained Learning Problems

Reference 22

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This paper cites Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Unresolved cited work

Reference 24

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Observation c68a2ede-e2a1-4ade-89d5-77c11c23f7e7 · outbound

This paper cites SAFE: Finding Sparse and Flat Minima to Improve Pruning.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints SAFE: Finding Sparse and Flat Minima to Improve Pruning

Reference 25

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This paper cites Constrained discrete diffusion.arXiv preprint arXiv:2503.09790, 2025.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Constrained discrete diffusion.arXiv preprint arXiv:2503.09790, 2025

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This paper cites Dual optimistic ascent (pi control) is the augmented lagrangian method in disguise.arXiv preprint arXiv:2509.22500, 2025.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Dual optimistic ascent (pi control) is the augmented lagrangian method in disguise.arXiv preprint arXiv:2509.22500, 2025

Reference 27

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This paper cites Al-cole: Augmented lagrangian for constrained learning.arXiv preprint arXiv:2510.20995, 2025.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Al-cole: Augmented lagrangian for constrained learning.arXiv preprint arXiv:2510.20995, 2025

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This paper cites Xstest: A test suite for identifying exaggerated safety behaviours in large language models.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Xstest: A test suite for identifying exaggerated safety behaviours in large language models

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This paper cites Safedpo: A simple approach to direct preference optimization with enhanced safety.arXiv preprint arXiv:2505.20065, 2025.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Safedpo: A simple approach to direct preference optimization with enhanced safety.arXiv preprint arXiv:2505.20065, 2025

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This paper cites Safety-tuned llamas: Lessons from improving the safety of large language models that follow instructions.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Safety-tuned llamas: Lessons from improving the safety of large language models that follow instructions

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Direct preference optimization: Your language model is secretly a reward model

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This paper cites Simpo: Simple preference optimization with a reference-free reward.Advances in Neural Information Processing Systems, 37:124198–124235, 2024.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Simpo: Simple preference optimization with a reference-free reward.Advances in Neural Information Processing Systems, 37:124198–124235, 2024

Reference 33

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

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation

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

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints SLiC-HF: Sequence Likelihood Calibration with Human Feedback

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This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks.Advances in neural information processing systems, 33:9459–9474, 2020.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Retrieval-augmented generation for knowledge-intensive nlp tasks.Advances in neural information processing systems, 33:9459–9474, 2020

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This paper cites Tyrrell Rockafellar.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Tyrrell Rockafellar

Reference 37

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This paper cites The augmented lagrangian methods: Overview and recent advances.arXiv preprint arXiv:2510.16827, 2025.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints The augmented lagrangian methods: Overview and recent advances.arXiv preprint arXiv:2510.16827, 2025

Reference 38

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Resilient constrained learning

Reference 39

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Rockafellar.Convex Analysis

Reference 40

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Wierzbicki and Stanislaw Kurcyusz

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This paper cites Reproducibility in machine-learning-based research: Overview, barriers, and drivers.AI Magazine, 46(2):e70002, 2025.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Reproducibility in machine-learning-based research: Overview, barriers, and drivers.AI Magazine, 46(2):e70002, 2025

Reference 42

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This paper cites Small language models are the future of agentic ai,.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Small language models are the future of agentic ai,

Reference 43

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Small Language Models are the Future of Agentic AI

Reference 44

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Unresolved cited work

Reference 45

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints When2call: When (not) to call tools

Reference 46

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This paper cites The berkeley function calling leaderboard (bfcl): From tool use to agentic evaluation of large language models.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints The berkeley function calling leaderboard (bfcl): From tool use to agentic evaluation of large language models

Reference 47

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Apigen: Generative api method recommendation

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints The Llama 3 Herd of Models

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This paper cites APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay

Reference 50

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Long is more for alignment: A simple but tough-to-beat baseline for instruction fine-tuning

Reference 51

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This paper cites Stanford alpaca: An instruction-following llama model, 2023.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Stanford alpaca: An instruction-following llama model, 2023

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This paper cites Beavertails: Towards improved safety alignment of LLM via a human-preference dataset.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Beavertails: Towards improved safety alignment of LLM via a human-preference dataset

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This paper cites Llama: Open and efficient foundation language models, 2023.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Llama: Open and efficient foundation language models, 2023

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Hashimoto

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Observation 7cda6654-ab36-45f5-a60c-e7a7a1c3c5a4 · outbound

This paper cites MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

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This paper cites Smarter, better, faster, longer: A modern bidirectional encoder for fast, memory efficient, and long context finetuning and inference.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Smarter, better, faster, longer: A modern bidirectional encoder for fast, memory efficient, and long context finetuning and inference

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Multi-Stage Document Ranking with BERT

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Observation fdf769ed-7d2b-41a4-a509-58afe0cb85ad · outbound

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints C- pack: Packed resources for general chinese embeddings

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints cross-encoder/ms-marco-minilm-l12-v2

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Pattern recognition using generalized portrait method.Automa- tion and Remote Control, 24:774–780, 1963

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Observation 7f2eb431-ff82-4cca-b863-2b3fdfdb925d · outbound

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Boser, Isabelle M

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Observation 11ce4227-2efe-4463-bd24-8e69c3721dc0 · outbound

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Support-vector networks.Machine learning, 20(3): 273–297, 1995

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints A theory of the risk for optimization with relaxation and its application to support vector machines.Journal of Machine Learning Research, 22(288): 1–38, 2021

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Observation 5fe9b98d-9e59-442c-b665-d098c3f6186b · outbound

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Non-convex scenario optimization.Mathematical Programming, 209(1):557–608, 2025

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Observation d136d787-d87b-45b3-8129-88f75475e84a · outbound

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Feasible Learning

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Observation c103c28f-6912-443b-96de-5a4ec779495a · outbound

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Reinforce- ment learning with almost sure constraints

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Observation 9714089f-cb75-4354-8823-83a205a13693 · outbound

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Anytime-constrained reinforcement learning

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Sauté rl: Almost surely safe reinforcement learning using state augmentation

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints On tilted losses in machine learning: Theory and applications.Journal of Machine Learning Research, 24(142):1–79, 2023

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Large-scale methods for distributionally robust optimization.Advances in neural information processing systems, 33: 8847–8860, 2020

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Probabilistically robust learning: Balancing average and worst-case performance

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Robust llm alignment via distributionally robust direct preference optimization

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Learning to summarize with human feedback

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Observation 1386ed92-90b4-4c02-954c-2e5f498a96e4 · outbound

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744, 2022

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Observation 98eb984c-4f54-4d6e-aa35-87962f7db8cd · outbound

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints RRHF: Rank Responses to Align Language Models with Human Feedback without tears

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Observation 06dc8911-1635-4799-9fdb-0dd499e8ec9d · outbound

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Repo: Understanding preference learning through relu-based optimization

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Observation 767b7f92-35bb-4e17-9875-c42a59bb9f9e · outbound

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints A general theoretical paradigm to understand learn- ing from human preferences

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Observation bb244f64-9f0a-4d66-b764-9f76e2840b47 · outbound

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Cal-dpo: Calibrated direct preference optimization for language model alignment.Advances in Neural Information Processing Systems, 37:114289–114320, 2024

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

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This paper cites Pku-saferlhf: Towards multi-level safety alignment for llms with human preference.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Pku-saferlhf: Towards multi-level safety alignment for llms with human preference

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Observation 0fc72b07-b3bc-4e54-9f9f-65e568344f4d · outbound

This paper cites Alignment of large language models with constrained learning.arXiv preprint arXiv:2505.19387, 2025.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Alignment of large language models with constrained learning.arXiv preprint arXiv:2505.19387, 2025

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Observation e7587c85-c14e-4947-a7e2-6590c0928b9e · outbound

This paper cites Cat-DPO: Category-Adaptive Safety Alignment.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Cat-DPO: Category-Adaptive Safety Alignment

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Observation 54debcdb-9c32-4bc0-b0c1-269c2620e896 · outbound

This paper cites Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models

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This paper cites MidPO: Dual Preference Optimization for Safety and Helpfulness in Large Language Models via a Mixture of Experts Framework.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints MidPO: Dual Preference Optimization for Safety and Helpfulness in Large Language Models via a Mixture of Experts Framework

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This paper cites Mitigating the safety alignment tax with null-space constrained policy optimization.arXiv preprint arXiv:2512.11391, 2025.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Mitigating the safety alignment tax with null-space constrained policy optimization.arXiv preprint arXiv:2512.11391, 2025

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Rule based rewards for fine-grained llm safety

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Observation 9f15dcca-a5e8-4def-b1a8-020fd12feaf5 · outbound

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Controllable preference optimization: Toward controllable multi-objective alignment

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints MetaAligner: Towards Generalizable Multi-Objective Alignment of Language Models

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Observation 76ba94d6-a07f-42d7-98db-23d78526c0e4 · outbound

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Nonlinear programming.Journal of the Operational Research Society, 48 (3):334–334, 1997

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Observation 7071ac4f-d1e3-48fd-8b45-db37e3fc8f19 · outbound

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Toolbehonest: A multi-level hallucination diagnostic benchmark for tool-augmented large language models

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints ToolSandbox: A stateful, conversational, interactive evaluation benchmark for LLM tool use capabilities

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Lora: Low-rank adaptation of large language models.Iclr, 1(2):3, 2022

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Decoupled Weight Decay Regularization

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming

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This paper cites ShieldGemma 2: Robust and Tractable Image Content Moderation.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints ShieldGemma 2: Robust and Tractable Image Content Moderation

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Observation 7639ac1e-f75c-4d23-b117-b90f0a7e4f43 · outbound

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Safety through reasoning: An empirical study of reasoning guardrail models.Findings of the Association for Computational Linguistics: EMNLP, 2025:21862–21880, 2025

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Observation beb30e19-143a-46d7-b44f-1f2c102ee3e5 · outbound

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints OR-bench: An over-refusal benchmark for large language models

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Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints kernel trick

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