Fine-tuning and adaptation

LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.

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arXiv AI

Sep 30

Ottilie: A Socially Intelligent Virtual Host for Sales-Driven Live Commerce

arXiv:2605.14542v2 Announce Type: replace Abstract: A skilled live-commerce host is not merely a narrator, but a sales agent who converts viewer curiosity into purchase intent through expert product...

By Yuyan Chen

llms rag agents fine-tuning safety

arXiv Machine Learning

Sep 30

Seeing What Should Be Heard: Diagnosing and Repairing Cross-Modal Shortcuts in Omni-Modal LLMs

arXiv:2609.36798v1 Announce Type: cross Abstract: Omni-modal large language models (LLMs) are expected to answer a question using the modality it explicitly refers to. However, existing training para...

By Yueran Ma, Ronghao Lin

llms nlp reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Sep 30

Same Bytes, Different Authority: Reserved-Token Representations in Chat-Template Prompt Injection

arXiv:2609.35932v1 Announce Type: cross Abstract: Prompt injection against LLM agents becomes much stronger when the injected instruction is wrapped in the model's own chat template. A forged templat...

By Yan Zhan, Yunze Song, Mengkai Hou, Wanting Zhang, Shaobo Liu, Zhijun Gao

llms rag agents nlp fine-tuning benchmarks

arXiv Machine Learning

Sep 30

Exploring Learning Models for Topological Relationship Recognition from Image Data

arXiv:2609.36172v1 Announce Type: cross Abstract: Figuring out how objects relate to each other, like whether they touch, overlap, stay completely separate or one sits inside another, matters a lot i...

By Saptak Das, Monidipa Das

computer-vision robotics fine-tuning

arXiv AI

Sep 30

BaLEEN: Biasing with Latent Encoded Entities for Context-Aware ASR

arXiv:2609.36913v1 Announce Type: cross Abstract: Transcribing domain-specific entities and rare proper nouns remains a major challenge in automatic speech recognition (ASR). In this paper, we propos...

By Chihiro Taguchi, Yotaro Kubo, Rujikorn Charakorn

llms fine-tuning multimodal safety

arXiv Machine Learning

Sep 30

Traversing the solution space of neural networks with Hessian Null Space Continuation

arXiv:2609.38081v1 Announce Type: new Abstract: On a single task, deep networks can learn many solutions, depending on their optimizer, training data, architecture, and hyperparameters. Many of these...

By Ann Huang, Mitchell Ostrow, Zhouyang Lu, William T. Redman, Leo Kozachkov, Kanaka Rajan

llms agents fine-tuning safety

arXiv Computation and Language

Sep 30

CypherTurn: A Multi-Turn Benchmark for Conversational Text-to-Cypher Evaluation and the Autonomy Divergence

arXiv:2609.36987v1 Announce Type: new Abstract: Graph databases are increasingly queried through natural language, yet every existing benchmark evaluates isolated single-turn queries rather than the...

By Yuzhe Zhang, Weijie Zhu, Haolin Yang, Ziyun Zhang, Xianwei Xue, Mengke Chen, Qiutong Pan, Huaqian Cai

agents fine-tuning benchmarks

arXiv AI

Sep 30

Safer Content or Firmer Refusals? A Hybrid Perturbation Defense for Alignment under Harmful Fine-tuning

arXiv:2609.36862v1 Announce Type: cross Abstract: Fine-tuning-as-a-service lets users adapt a safety-aligned language model to their own data, but it also creates a harmful fine-tuning attack surface...

By Muhammad Zeeshan Akram, Mufid Kamel Marican, Anvesh Reddy Yenugu, Ali Zain Kaimkhani, Minghong Fang

llms rag fine-tuning safety

arXiv Computation and Language

Sep 30

Pruning for Efficiency, Paying in Fairness: Demographic Disparities in Pruned Speech-LLMs

arXiv:2609.38106v1 Announce Type: cross Abstract: Speech-LLMs are expensive to run, making compression important for real-world deployment. However, compressed models are usually selected using aggre...

By Ganesh Pavan Kartikeya Bharadwaj Kolluri, Michael Kampouridis, Ravi Shekhar

llms fine-tuning efficiency multimodal safety

arXiv Computation and Language

Sep 30

Who Warmed the Archives? LLMs Overestimate Historical Warmth

arXiv:2609.37499v1 Announce Type: new Abstract: Historical archives are an under-used source for extending the instrumental climate record backward in time, and LLMs offer a way to extract the indice...

By Claudiu Creanga, Liviu P. Dinu

llms fine-tuning benchmarks safety

arXiv AI

Sep 30

Correct, Don't Delete: Mitigating Emergent Misalignment with Corrective Supervision

arXiv:2609.37624v1 Announce Type: cross Abstract: Fine-tuning a language model on a narrow set of harmful demonstrations, such as bad medical advice, can make it broadly misaligned on unrelated quest...

By Jacob Epifano

llms fine-tuning

arXiv AI

Sep 30

Do-JEPA: From Masking to Intervention in Latent World Models

arXiv:2609.37378v1 Announce Type: cross Abstract: Latent world models are trained to predict what happens next, so nothing in their objective separates what an action caused from what merely co-occur...

By Hossein Resani, Javen Qinfeng Shi

fine-tuning benchmarks

arXiv Machine Learning

Sep 30

Backpropagated Output Momentum: Relocating Optimizer History from Parameters to Task Space

arXiv:2609.36738v1 Announce Type: new Abstract: Optimizer momentum is usually stored as a parameter-sized moving average of past gradients, which makes history costly and fixes each past signal in th...

By Yuchen Li, Zongqi Fan, Nguyen H. Tran, Ken-Tye Yong

fine-tuning

arXiv Computation and Language

Sep 30

Video2Skill: From Streaming Experience to Reusable Embodied Skills

arXiv:2609.36691v1 Announce Type: new Abstract: Manipulation behaviors vary widely across objects and scenes, but they share a small set of reusable skills, and planning with these skills helps embod...

By Jianshu Zhang, Ce Zhang, Xiyuan Yang, Chenwei Xu, Haoran Lu, Yijiang Li, Yaqi Xie, Katia P. Sycara, Han Liu

llms agents robotics fine-tuning multimodal benchmarks

arXiv AI

Sep 30

On Trajectory-Aware Training for Masked Diffusion Language Models

arXiv:2609.37974v1 Announce Type: cross Abstract: Masked diffusion models (MDMs) generate text by unmasking several tokens per step, but they are trained and sampled under different conditions. The m...

By Manuel Madeira, Amitis Shidani, Alice Bizeul, Victor Turrisi, Louis B\'ethune, Bhavika Devnani, Dan Busbridge, Pierre Ablin, Jo\~ao Monteiro

llms diffusion fine-tuning safety

arXiv Machine Learning

Sep 30

Fine-Tuning on Self-Generated and Reward-Weighted Data: Learning Dynamics, Convergence Rates, and Benefits of Off-Policyness

arXiv:2609.36945v1 Announce Type: new Abstract: We study the learning dynamics of fine-tuning a policy model on self-generated and reward-weighted data, with particular focus on a generalized version...

By Zhiwei Wang, Yanxi Chen, Yaliang Li, Bolin Ding

llms reinforcement-learning fine-tuning

arXiv AI

Sep 30

Scaling Influence Functions in LLMs through Eigenbasis-Corrected One-Bit Gradient Projection

arXiv:2609.37842v1 Announce Type: cross Abstract: Influence functions estimate how individual training examples affect the behavior of large language models (LLMs). Analyzing how training data influe...

By Jaeseung Heo, J Rosser, Dongwoo Kim

llms fine-tuning efficiency

arXiv Computation and Language

Sep 30

Learning from Teacher Continuations at Student States

arXiv:2609.36246v1 Announce Type: new Abstract: We present OLIVE (OnLine InterVEntion). At each iteration, the evolving student policy generates a new prefix, the teacher continues it autoregressivel...

By Haojin Wang, Dylan Zhang, Huaibo Chen, Suhao Yu, Yihang Sun, Zhanyang Jin, Jiaying Ye, Dianqi Li, Prasanna Sattigeri, Kamal Youcef-Toumi, Hao Peng

llms agents fine-tuning efficiency

arXiv Computation and Language

Sep 30

Layer-Informed Fine-Tuning via Three-Stage Functional Segmentation of LLMs

The paper proposes Layer-Informed Fine-Tuning (LIFT), a method that identifies and updates only the most functionally critical layers of large language models (LLMs) using a bottleneck identification mechanism based on sensitivity analysis. By focusing on layers that handle conceptualization, reasoning, and textualization, LIFT aims to accelerate training and enhance performance on reasoning tasks. Experiments demonstrate that this selective fine-tuning approach both speeds up the training process and yields significant performance gains.

By Junning Shao, Siwei Wang, Zhixuan Fang

llms computer-vision fine-tuning benchmarks

arXiv AI

Sep 30

Diffusion Policy Improvement with Proposal-Conditioned Refinement Flows

arXiv:2609.36812v1 Announce Type: cross Abstract: Diffusion and flow policies can model complex behaviors in offline reinforcement learning (RL). However, penalizing their KL divergence from the beha...

By Junhyun Ha, Juho Lee, Byungwoo Park

diffusion reinforcement-learning fine-tuning

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