In-Place Test-Time Training

• URL: http://arxiv.org/abs/2604.06169v1
• Date: Tue, 07 Apr 2026 17:59:44 GMT
• Title: In-Place Test-Time Training
• Abstract summary: In-Place Test-Time Training (In-Place TTT) is a framework that seamlessly endows Large Language Models with Test-Time Training.<n>In-Place TTT treats the final projection matrix of the ubiquitous blocks as its adaptable fast weights.<n>We replace TTT's generic reconstruction objective with a tailored, theoretically-grounded objective aligned with the Next-Token-Prediction task.
• Score: 32.521599123691026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The static ``train then deploy" paradigm fundamentally limits Large Language Models (LLMs) from dynamically adapting their weights in response to continuous streams of new information inherent in real-world tasks. Test-Time Training (TTT) offers a compelling alternative by updating a subset of model parameters (fast weights) at inference time, yet its potential in the current LLM ecosystem is hindered by critical barriers including architectural incompatibility, computational inefficiency and misaligned fast weight objectives for language modeling. In this work, we introduce In-Place Test-Time Training (In-Place TTT), a framework that seamlessly endows LLMs with Test-Time Training ability. In-Place TTT treats the final projection matrix of the ubiquitous MLP blocks as its adaptable fast weights, enabling a ``drop-in" enhancement for LLMs without costly retraining from scratch. Furthermore, we replace TTT's generic reconstruction objective with a tailored, theoretically-grounded objective explicitly aligned with the Next-Token-Prediction task governing autoregressive language modeling. This principled objective, combined with an efficient chunk-wise update mechanism, results in a highly scalable algorithm compatible with context parallelism. Extensive experiments validate our framework's effectiveness: as an in-place enhancement, it enables a 4B-parameter model to achieve superior performance on tasks with contexts up to 128k, and when pretrained from scratch, it consistently outperforms competitive TTT-related approaches. Ablation study results further provide deeper insights on our design choices. Collectively, our results establish In-Place TTT as a promising step towards a paradigm of continual learning in LLMs.

Related papers

• Matching Features, Not Tokens: Energy-Based Fine-Tuning of Language Models [102.20309135516186]
  Cross-entropy (CE) training provides dense and scalable supervision for language models.<n>We introduce a feature-matching objective for language-model fine-tuning.<n>To optimize this objective efficiently, we propose energy-based fine-tuning.
  arXiv  Detail & Related papers  (2026-03-12T17:57:50Z)
• DiRL: An Efficient Post-Training Framework for Diffusion Language Models [54.405206032785706]
  Diffusion Language Models (dLLMs) have emerged as promising alternatives to Auto-Regressive (AR) models.<n>Existing methods suffer from computational inefficiency and objective mismatches between training and inference.<n>We introduce DiRL, an efficient post-training framework that tightly integrates FlexAttention-accelerated blockwise training with LMDeploy-optimized inference.
  arXiv  Detail & Related papers  (2025-12-23T08:33:19Z)
• TaoSearchEmb: A Multi-Objective Reinforcement Learning Framework for Dense Retrieval in Taobao Search [11.893855231479717]
  Retrieval-GRPO is a reinforcement learning-based dense retrieval framework.<n>It has been deployed on China's largest e-commerce platform.
  arXiv  Detail & Related papers  (2025-11-17T20:16:52Z)
• Test time training enhances in-context learning of nonlinear functions [51.56484100374058]
  Test-time training (TTT) enhances model performance by explicitly updating designated parameters prior to each prediction.<n>We investigate the combination of TTT with in-context learning (ICL), where the model is given a few examples from the target distribution at inference time.
  arXiv  Detail & Related papers  (2025-09-30T03:56:44Z)
• Post-Completion Learning for Language Models [20.589364712188015]
  Current language model training paradigms terminate learning upon reaching the end-of-sequence (eos>) token.<n>We propose Post-Completion Learning (PCL), a novel training framework that systematically utilizes the sequence space after model output completion.<n>PCL enables models to continue generating self-assessments and reward predictions during training, while maintaining efficient inference by stopping at the completion point.
  arXiv  Detail & Related papers  (2025-07-27T12:47:26Z)
• Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation [67.80294336559574]
  Continual Test Time Adaptation (CTTA) is a task that requires a source pre-trained model to continually adapt to new scenarios.<n>We propose a novel pipeline, Orthogonal Projection Subspace to aggregate online Prior-knowledge, dubbed OoPk.
  arXiv  Detail & Related papers  (2025-06-23T18:17:39Z)
• SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scaling [39.57154199908565]
  Self-Enhanced Test-Time Scaling (SETS) is a simple yet effective approach that overcomes limitations by strategically combining parallel and sequential techniques.<n>SETS exploits the inherent self-verification and self- computation capabilities of Large Language Models, unifying sampling, verification, and correction within a single framework.<n>Our results show SETS achieves significant performance improvements and more advantageous test-time scaling behavior than the alternatives.
  arXiv  Detail & Related papers  (2025-01-31T17:03:16Z)
• Test-Time Training on Graphs with Large Language Models (LLMs) [68.375487369596]
  Test-Time Training (TTT) has been proposed as a promising approach to train Graph Neural Networks (GNNs) Inspired by the great annotation ability of Large Language Models (LLMs) on Text-Attributed Graphs (TAGs), we propose to enhance the test-time training on graphs with LLMs as annotators. A two-stage training strategy is designed to tailor the test-time model with the limited and noisy labels.
  arXiv  Detail & Related papers  (2024-04-21T08:20:02Z)
• Task-Distributionally Robust Data-Free Meta-Learning [99.56612787882334]
  Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple pre-trained models without requiring their original training data. For the first time, we reveal two major challenges hindering their practical deployments: Task-Distribution Shift ( TDS) and Task-Distribution Corruption (TDC)
  arXiv  Detail & Related papers  (2023-11-23T15:46:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.