anthology-of-the-sota

adaptation-and-tuning

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128 items with this tag.

Oct 06, 2026
LIT-799: Olmo Hybrid: From Theory to Practice and Back
Oct 06, 2026
LIT-801: Cracks in the Foundation: Seemingly Minor Architectural Choices Impact Long Context Extension
Oct 03, 2026
LIT-773: Improving Video Generation with Human Feedback
Oct 03, 2026
LIT-774: Diffusion Model Alignment Using Direct Preference Optimization
- adaptation-and-tuning
- generative-modeling
Oct 03, 2026
LIT-776: Training Diffusion Models with Reinforcement Learning
Oct 03, 2026
LIT-778: DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
- adaptation-and-tuning
- generative-modeling
Oct 03, 2026
LIT-779: Flow-GRPO: Training Flow Matching Models via Online RL
Oct 03, 2026
LIT-782: Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation
Oct 03, 2026
LIT-788: ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation
Oct 03, 2026
LIT-795: Scaling Laws for Reward Model Overoptimization
Oct 03, 2026
LIT-797: Aligning Text-to-Image Models using Human Feedback
Oct 03, 2026
SOTA-442: To RL-tune a flow-matching generator, roll it out as a same-marginal SDE built from its own velocity, and anchor each step with the closed-form KL to the reference
Oct 03, 2026
SOTA-445: When the base text-to-image diffusion model is better than the models that generated the preference pairs, train on the pairs with Diffusion-DPO rather than fine-tuning on the preferred images
- adaptation-and-tuning
- generative-modeling
Oct 03, 2026
SOTA-449: When reward-tuning a classifier-free-guided diffusion model over several rounds of sampling, train the guided prediction at the fixed sampling guidance weight, not the conditional prediction alone
- adaptation-and-tuning
- generative-modeling
Oct 03, 2026
THEORY-122: A diffusion or flow sampler can be trained by a likelihood-ratio policy gradient only when each step is stochastic with a tractable density, and a same-marginal SDE built from the trained velocity supplies that without changing the marginals
Oct 01, 2026
LIT-734: Fine-Tuning Language Models from Human Preferences
- adaptation-and-tuning
- analysis-and-evaluation
Oct 01, 2026
LIT-744: Text Embeddings by Weakly-Supervised Contrastive Pre-training
Oct 01, 2026
LIT-755: Wonderland: Navigating 3D Scenes from a Single Image
Oct 01, 2026
LIT-757: Promptagator: Few-shot Dense Retrieval From 8 Examples
Oct 01, 2026
LIT-762: Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models
- adaptation-and-tuning
- distributed-optimization
Oct 01, 2026
LIT-769: Structural Decoupling: A Scaffold-Flow Theory of Generalization and Alignment
Sep 25, 2026
LIT-670: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Sep 25, 2026
LIT-674: Robust fine-tuning of zero-shot models
Sep 25, 2026
LIT-675: Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Sep 25, 2026
LIT-687: Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think
Sep 25, 2026
LIT-691: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Sep 25, 2026
LIT-702: Simple and Effective Masked Diffusion Language Models
Sep 25, 2026
LIT-715: Surya: Foundation Model for Heliophysics
Sep 25, 2026
SOTA-407: Interpolate the zero-shot and fine-tuned weights at about half way rather than shipping the fine-tuned model
Sep 25, 2026
SOTA-409: Average the fine-tuned models from your hyperparameter sweep instead of keeping only the best, adding each one in validation order and only if it helps
- training-optimization
- adaptation-and-tuning
Sep 25, 2026
SOTA-414: To make an image diffusion model a dense geometric predictor, fine-tune it end to end as a one-step model at t = T with a task loss
Sep 25, 2026
SOTA-426: When you merge and reinitialise a low-rank adapter mid-training, prune the optimizer state and re-warm the learning rate from zero — both, not either
Sep 24, 2026
LIT-621: Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
Sep 24, 2026
LIT-624: Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model
Sep 24, 2026
LIT-633: AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
Sep 24, 2026
LIT-647: Sequence Level Training with Recurrent Neural Networks
Sep 24, 2026
LIT-650: Competition-Level Code Generation with AlphaCode
Sep 24, 2026
SOTA-393: Freeze the image model's spatial layers when adding time if checkpoint compatibility matters; do not expect a quality gain from it
Sep 23, 2026
LIT-574: Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models
- analysis-and-evaluation
- adaptation-and-tuning
Sep 23, 2026
LIT-576: Mass-Editing Memory in a Transformer
- adaptation-and-tuning
- analysis-and-evaluation
Sep 23, 2026
LIT-577: Evaluating the Ripple Effects of Knowledge Editing in Language Models
Sep 23, 2026
LIT-578: Locating and Editing Factual Associations in GPT
- analysis-and-evaluation
- adaptation-and-tuning
Sep 23, 2026
SOTA-347: To write many facts into a model's weights, update a range of early-to-mid MLP layers jointly in one batched solve (MEMIT), choosing the range by measured edit success rather than causal tracing
- adaptation-and-tuning
- analysis-and-evaluation
Sep 23, 2026
SOTA-348: Evaluate a knowledge edit on the facts it implies — logical consequences, multi-hop compositions, aliases and the subject's other facts — not only on the edited prompt, its paraphrases and unrelated neighbours
- analysis-and-evaluation
- adaptation-and-tuning
Sep 23, 2026
THEORY-082: Causal tracing shows where a language model stores a fact, and that is where the fact should be edited
- analysis-and-evaluation
- adaptation-and-tuning
Sep 22, 2026
LIT-516: From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications
Sep 22, 2026
LIT-536: Do pretrained Transformers Learn In-Context by Gradient Descent?
Sep 22, 2026
LIT-549: ReFT: Representation Finetuning for Language Models
- adaptation-and-tuning
- analysis-and-evaluation
Sep 22, 2026
SOTA-318: Choose the rank per matrix when you compress a transformer, because low-rank structure varies by component and by depth
- inference-optimization
- adaptation-and-tuning
Sep 22, 2026
SOTA-323: Test a claim about what pretraining produces on a model trained with the pretraining objective, not one trained on the task family you are testing
Sep 22, 2026
SOTA-329: For short-output tasks, adapt a frozen model with a low-rank intervention on a few positions of the residual stream, not a weight adapter; do not use it for long chain-of-thought
- adaptation-and-tuning
Sep 21, 2026
LIT-467: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
- in-context-learning
- adaptation-and-tuning
Sep 21, 2026
LIT-473: Quantized Evolution Strategies: High-precision Fine-tuning of Quantized LLMs at Low-precision Cost
Sep 21, 2026
LIT-474: The Coverage Principle: How Pre-Training Enables Post-Training
Sep 21, 2026
LIT-488: gen2seg: Generative Models Enable Generalizable Instance Segmentation
Sep 21, 2026
LIT-491: Noise Hypernetworks: Amortizing Test-Time Compute in Diffusion Models
Sep 21, 2026
LIT-505: Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Sep 21, 2026
LIT-506: Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions
Sep 21, 2026
SOTA-279: Put worked reasoning steps in the few-shot exemplars when the task needs more than one step, and only once the model is large enough
Sep 21, 2026
SOTA-283: To fine-tune a model that is already quantized, bank the part of each update that is smaller than the lattice spacing, and rebuild the accumulator from seeds rather than storing it
Sep 21, 2026
SOTA-284: Choose the checkpoint you post-train from by how much mass it puts on good rare responses, not by validation cross-entropy
- analysis-and-evaluation
- adaptation-and-tuning
Sep 21, 2026
SOTA-299: Finetune the whole generative model, encoder and decoder, for a dense perception task instead of attaching a head to a backbone
Sep 21, 2026
SOTA-302: Steer a distilled generator by modulating its input noise, not by fine-tuning its weights
Sep 19, 2026
LIT-432: Finetuned Language Models Are Zero-Shot Learners
- adaptation-and-tuning
- data-pipeline
Sep 19, 2026
LIT-433: Learning to summarize from human feedback
- adaptation-and-tuning
- analysis-and-evaluation
Sep 19, 2026
LIT-434: Deep reinforcement learning from human preferences
- adaptation-and-tuning
Sep 18, 2026
LIT-425: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Sep 17, 2026
LIT-378: QLoRA: Efficient Finetuning of Quantized LLMs
Sep 17, 2026
LIT-379: The Surprising Effectiveness of Test-Time Training for Few-Shot Learning
Sep 17, 2026
LIT-381: Scaling Diffusion Language Models via Adaptation from Autoregressive Models
- generative-modeling
- adaptation-and-tuning
Sep 17, 2026
LIT-382: Large Language Models Cannot Self-Correct Reasoning Yet
- analysis-and-evaluation
- adaptation-and-tuning
Sep 17, 2026
LIT-383: Training Language Models to Self-Correct via Reinforcement Learning
- adaptation-and-tuning
Sep 17, 2026
SOTA-230: Quantize the frozen base to 4-bit and keep the adapters in 16-bit
Sep 17, 2026
SOTA-231: Put an adapter on every linear layer, not just the attention query and value projections
- adaptation-and-tuning
Sep 17, 2026
SOTA-233: Train self-correction with multi-turn online RL on the model's own traces, rather than prompting for it
- adaptation-and-tuning
Sep 17, 2026
SOTA-235: Update the weights at inference on the test instance when the task is structurally novel
- adaptation-and-tuning
Sep 16, 2026
LIT-377: Training language models to follow instructions with human feedback
- adaptation-and-tuning
Sep 15, 2026
LIT-230: Understanding Evolution Strategies for LLM Reasoning: Broader Reasoning Coverage than GRPO
Sep 15, 2026
LIT-231: Hyper-ES: Effective Evolution Strategies for LLM Reasoning via Descent Direction Merging
- adaptation-and-tuning
- training-optimization
Sep 15, 2026
LIT-233: Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights
Sep 15, 2026
LIT-234: Beyond the Best Guess: Improving LLM Solution Coverage with Evolution Strategies
- adaptation-and-tuning
- training-optimization
Sep 15, 2026
LIT-235: Matching Accuracy, Different Geometry: Evolution Strategies vs GRPO in LLM Post-Training
Sep 15, 2026
LIT-236: The Blessing of Dimensionality in LLM Fine-tuning: A Variance-Curvature Perspective
Sep 15, 2026
LIT-237: Evolutionary Strategies lead to Catastrophic Forgetting in LLMs
- adaptation-and-tuning
- training-optimization
Sep 15, 2026
LIT-238: Overcoming Forgetting in LLM Fine-Tuning with Evolution Strategies
- adaptation-and-tuning
- training-optimization
Sep 15, 2026
LIT-239: ESSA: Evolutionary Strategies for Scalable Alignment
- adaptation-and-tuning
- training-optimization
Sep 15, 2026
LIT-240: ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning
- adaptation-and-tuning
- training-optimization
Sep 15, 2026
SOTA-210: Report pass@k as well as pass@1 after post-training: reinforcement learning raises one and lowers the other
- analysis-and-evaluation
- adaptation-and-tuning
Sep 15, 2026
SOTA-212: Post-train by scoring many random weight perturbations in one parallel pass and majority-voting the best of them
- adaptation-and-tuning
Sep 15, 2026
SOTA-213: Control evolution-strategies drift with a larger population or an anchor penalty, not by stopping training early
- adaptation-and-tuning
Sep 10, 2026
SOTA-199: Regularize a narrow fine-tune against the pre-fine-tuning model's own samples
- adaptation-and-tuning
Sep 10, 2026
SOTA-201: Give each pretrained component its own learning rate when fine-tuning a composite model
- adaptation-and-tuning
Sep 08, 2026
SOTA-184: Adapt a pretrained model by training a low-rank update to each weight matrix, not the matrix itself
- adaptation-and-tuning
Sep 07, 2026
LIT-211: Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning
- adaptation-and-tuning
- training-optimization
Sep 07, 2026
SOTA-145: Estimate the RL baseline from a group of samples for the same prompt instead of training a critic
- adaptation-and-tuning
Sep 07, 2026
SOTA-146: Correct the GRPO objective before running it — the published form has three independently identified defects
- adaptation-and-tuning
Sep 07, 2026
SOTA-154: Fine-tune with evolution strategies instead of policy-gradient reinforcement learning
- adaptation-and-tuning
Sep 05, 2026
LIT-119: Falcon-H1-Tiny: A series of extremely small, yet powerful language models redefining capabilities at small scale
Sep 05, 2026
LIT-127: DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Sep 05, 2026
LIT-128: Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling
- adaptation-and-tuning
Sep 05, 2026
LIT-129: Can Tiny Language Models Reason?
Sep 05, 2026
LIT-130: Olmo 3
- adaptation-and-tuning
Sep 05, 2026
LIT-164: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- adaptation-and-tuning
Sep 05, 2026
LIT-167: Understanding R1-Zero-Like Training: A Critical Perspective
- analysis-and-evaluation
- adaptation-and-tuning
Sep 05, 2026
LIT-168: DAPO: An Open-Source LLM Reinforcement Learning System at Scale
- adaptation-and-tuning
Sep 05, 2026
LIT-169: Direct Preference Optimization: Your Language Model is Secretly a Reward Model
- adaptation-and-tuning
Sep 05, 2026
LIT-172: Tulu 3: Pushing Frontiers in Open Language Model Post-Training
- adaptation-and-tuning
Sep 05, 2026
LIT-180: Group Sequence Policy Optimization
- adaptation-and-tuning
Sep 05, 2026
SOTA-123: For tiny specialized models, pretrain from scratch on the target SFT or reasoning data instead of pretrain-then-finetune
- adaptation-and-tuning
- tiny-models
Sep 05, 2026
SOTA-126: Run DPO on tiny models for one epoch only
- adaptation-and-tuning
- tiny-models
Sep 05, 2026
SOTA-129: Build a reasoning model in three stages: pretrain on general data, SFT on reasoning traces, then RL with verifiable rewards
- adaptation-and-tuning
Sep 05, 2026
SOTA-130: Skip the reasoning SFT stage and run RL with verifiable rewards directly on the base model
- adaptation-and-tuning
Aug 24, 2026
LIT-034: Don't Stop Pretraining: Adapt Language Models to Domains and Tasks
- adaptation-and-tuning
Aug 24, 2026
LIT-035: Language Models are Few-Shot Learners
Aug 24, 2026
LIT-046: LoRA: Low-Rank Adaptation of Large Language Models
- adaptation-and-tuning
Aug 24, 2026
LIT-049: Learning to Prompt for Vision-Language Models
Aug 24, 2026
LIT-055: P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
- adaptation-and-tuning
Aug 24, 2026
LIT-072: Simple Open-Vocabulary Object Detection with Vision Transformers
Aug 24, 2026
LIT-078: An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
- adaptation-and-tuning
Aug 24, 2026
LIT-079: DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
- adaptation-and-tuning
Aug 24, 2026
LIT-082: Constitutional AI: Harmlessness from AI Feedback
- adaptation-and-tuning
- data-pipeline
Aug 24, 2026
LIT-089: Adding Conditional Control to Text-to-Image Diffusion Models
- adaptation-and-tuning
Aug 24, 2026
LIT-091: T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models
- adaptation-and-tuning
Aug 24, 2026
LIT-104: ReLoRA: High-Rank Training Through Low-Rank Updates
- training-optimization
- adaptation-and-tuning
Aug 24, 2026
LIT-118: PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
- adaptation-and-tuning
Aug 24, 2026
SOTA-033: continued pre-training for fine tuning
- adaptation-and-tuning
Aug 24, 2026
SOTA-038: ICL permits few-shot task adaptability
- in-context-learning
- adaptation-and-tuning
Aug 24, 2026
SOTA-051: Initialize a residual or adapter branch to exactly zero, not merely near zero
- model-stability
- adaptation-and-tuning

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