Training Language Models to Reason Efficiently

• URL: http://arxiv.org/abs/2502.04463v2
• Date: Tue, 11 Feb 2025 18:06:02 GMT
• Title: Training Language Models to Reason Efficiently
• Authors: Daman Arora, Andrea Zanette,
• Abstract summary: We use reinforcement learning to train large reasoning models to reason efficiently. Our method incentivizes models to minimize unnecessary computational overhead while maintaining accuracy. Experiments on two open-weight large reasoning models demonstrate significant reductions in inference cost while preserving most of the accuracy.
• Score: 14.390800014819439
• License:
• Abstract: Scaling model size and training data has led to great advances in the performance of Large Language Models (LLMs). However, the diminishing returns of this approach necessitate alternative methods to improve model capabilities, particularly in tasks requiring advanced reasoning. Large reasoning models, which leverage long chain-of-thoughts, bring unprecedented breakthroughs in problem-solving capabilities but at a substantial deployment cost associated to longer generations. Reducing inference costs is crucial for the economic feasibility, user experience, and environmental sustainability of these models. In this work, we propose to train large reasoning models to reason efficiently. More precisely, we use reinforcement learning (RL) to train reasoning models to dynamically allocate inference-time compute based on task complexity. Our method incentivizes models to minimize unnecessary computational overhead while maintaining accuracy, thereby achieving substantial efficiency gains. It enables the derivation of a family of reasoning models with varying efficiency levels, controlled via a single hyperparameter. Experiments on two open-weight large reasoning models demonstrate significant reductions in inference cost while preserving most of the accuracy.

Related papers

• Reusing Embeddings: Reproducible Reward Model Research in Large Language Model Alignment without GPUs [58.18140409409302]
  Large Language Models (LLMs) have made substantial strides in structured tasks through Reinforcement Learning (RL) Applying RL in broader domains like chatbots and content generation presents unique challenges. We show a case study of reproducing existing reward model ensemble research using embedding-based reward models.
  arXiv  Detail & Related papers  (2025-02-04T19:37:35Z)
• Patience Is The Key to Large Language Model Reasoning [0.0]
  We propose a simple method by encouraging models to adopt a more patient reasoning style. We generate detailed reasoning processes as positive examples and simple answers as negative examples, thereby training the model to favor thoroughness in its responses. Our results demonstrate a performance increase of up to 2.1% on GSM8k with training just on a lightweight dataset.
  arXiv  Detail & Related papers  (2024-11-20T07:20:48Z)
• DistiLLM: Towards Streamlined Distillation for Large Language Models [53.46759297929675]
  DistiLLM is a more effective and efficient KD framework for auto-regressive language models. DisiLLM comprises two components: (1) a novel skew Kullback-Leibler divergence loss, where we unveil and leverage its theoretical properties, and (2) an adaptive off-policy approach designed to enhance the efficiency in utilizing student-generated outputs.
  arXiv  Detail & Related papers  (2024-02-06T11:10:35Z)
• EsaCL: Efficient Continual Learning of Sparse Models [10.227171407348326]
  Key challenge in the continual learning setting is to efficiently learn a sequence of tasks without forgetting how to perform previously learned tasks. We propose a new method for efficient continual learning of sparse models (EsaCL) that can automatically prune redundant parameters without adversely impacting the model's predictive power.
  arXiv  Detail & Related papers  (2024-01-11T04:59:44Z)
• Fast-ELECTRA for Efficient Pre-training [83.29484808667532]
  ELECTRA pre-trains language models by detecting tokens in a sequence that have been replaced by an auxiliary model. We propose Fast-ELECTRA, which leverages an existing language model as the auxiliary model. Our approach rivals the performance of state-of-the-art ELECTRA-style pre-training methods, while significantly eliminating the computation and memory cost brought by the joint training of the auxiliary model.
  arXiv  Detail & Related papers  (2023-10-11T09:55:46Z)
• Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs [5.488334211013093]
  We show that learning an actuated model in parallel to training the RL agent significantly reduces the total amount of required data sampled from the real system. We also show that iteratively updating the model is of major importance to avoid biases in the RL training.
  arXiv  Detail & Related papers  (2023-02-14T16:14:39Z)
• Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning [65.268245109828]
  In data-rich domains such as vision, language, and speech, deep learning prevails to deliver high-performance task-specific models. Deep learning in resource-limited domains still faces multiple challenges including (i) limited data, (ii) constrained model development cost, and (iii) lack of adequate pre-trained models for effective finetuning. Model reprogramming enables resource-efficient cross-domain machine learning by repurposing a well-developed pre-trained model from a source domain to solve tasks in a target domain without model finetuning.
  arXiv  Detail & Related papers  (2022-02-22T02:33:54Z)
• Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems [7.415129876303651]
  Model pruning is an effective technique to reduce computation overhead for deep neural networks by removing redundant parameters. Modern recommendation systems are still thirsty for model capacity due to the demand for handling big data. We propose a dynamic training scheme, namely alternate model growth and pruning, to alternatively construct and prune weights in the course of training.
  arXiv  Detail & Related papers  (2021-05-04T03:14:30Z)
• Sufficiently Accurate Model Learning for Planning [119.80502738709937]
  This paper introduces the constrained Sufficiently Accurate model learning approach. It provides examples of such problems, and presents a theorem on how close some approximate solutions can be. The approximate solution quality will depend on the function parameterization, loss and constraint function smoothness, and the number of samples in model learning.
  arXiv  Detail & Related papers  (2021-02-11T16:27:31Z)
• Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers [94.43313684188819]
  We study the impact of model size in this setting, focusing on Transformer models for NLP tasks that are limited by compute. We first show that even though smaller Transformer models execute faster per iteration, wider and deeper models converge in significantly fewer steps. This leads to an apparent trade-off between the training efficiency of large Transformer models and the inference efficiency of small Transformer models.
  arXiv  Detail & Related papers  (2020-02-26T21:17:13Z)

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.