This work employs encoder-decoder networks built around long-short term memory (LSTM) cells in a distributed optimization algorithm that allows unprecedented reconstruction accuracy or the usage of different prediction-horizon lengths without the necessity to retrain.
Abstract
The communication demands of distributed model prediction control (DMPC) can overwhelm even advanced wireless communication technologies as agents must exchange a significant amount of information at least once per time step. To semantically reduce communication demands, this work employs encoder-decoder networks built around long-short term memory (LSTM) cells in a distributed optimization algorithm. Agents publish a reduced representation of a message and receivers reconstruct the original message upon reception. In tests with reduced communication using formations of mobile robots, trained networks retain satisfactory performance and work reliably under conditions overwhelming full communication. As the results show, the usage of LSTMs either allows unprecedented reconstruction accuracy or the usage of different prediction-horizon lengths without the necessity to retrain.
Structured Experience Compression (SEC), a lightweight method that distills each trajectory into a four-field unit encoding the task type, key decisions, failure reason, and repair action, is proposed, suggesting potential suitability for long-lived deployment settings.
Zhizhen Zhu, Ruochen Fang, Rong Hu· 2026 7th International Confe...· 2 citations
Deploying large language models in resource-constrained environments such as mobile devices requires effective compression. Pruning and quantization are largely used, yet applying pruning to a new model typically demands manual inspection of its architecture to identify layer names, MLP structure, and projection patter...
Gyanko Issah Yussif, Tasnia Sultana, Mohamed Ali et al.· International Conference on...· 0 citations
Large Language Models (LLMs) trained using Chain-of-Thought (CoT) supervision have achieved state-of-the-art performance on complex reasoning tasks. However, the generation of long reasoning chains introduces substantial computational overhead during inference, limiting their deployment in low-latency and resource-cons...
V. A, Mithaguru, Amrita Kundu et al.· 2026 4th International Confe...· 0 citations
This study introduces CodeGrep, a 14B retrieval agent trained end-to-end with GRPO to issue multi-turn parallel grep, glob, and read tool calls and return candidate files to a frozen downstream coding agent, and applies the efficiency signal at the advantage layer rather than the reward layer to reduce KL drift and tra...
Wu-Ya Chen, Yihao Yang, Yang Cao et al.· 2 citations
This work studies value-controllable alignment through discrete condition vectors and proposes Verifiable-reward-Routed LoRA—a parameter-efficient mixture-of-experts LoRA framework enhanced with conditioned gating, which consistently out-performs prompt-based steering and multi-task PEFT baselines.
Jing Wang, Yaomin Wu, Yinglin Wang et al.· Annual Meeting of the Associ...· 0 citations
CoACT is built on next-action preservation (NAP), which requires a compressed observation to induce the same next action as the raw observation, and uses an action-preservation reward based on NAP to filter out candidates that would change the agent's next action.
Hao Chen, Yuancheng Zhu, Yitong Zhang et al.· 1 citation
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
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