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reproducible-research

topic2 events
papersTODAY 04:00 UTC

GzDRL: Single-process deep reinforcement learning framework for Gazebo

Researchers introduce GzDRL, a reinforcement learning framework that runs Gazebo robotics simulations in a single process rather than relying on conventional middleware-based bridges. The authors say this design targets long-standing obstacles to reproducible and scalable robotics experimentation. The work is posted as an arXiv preprint (2609.13243).

robotics/reinforcement-learning/simulation/reproducibility
papersSEP 10 04:00 UTC

RiLM paper proposes geodesic decoding to cut output-layer cost in sub-million-parameter language models

A new arXiv paper introduces RiLM, a method for training language models with under one million parameters, targeting use cases such as edge deployment, domain adaptation, and reproducible research. The authors note that a small two-layer LSTM or Transformer with 128-dimensional embeddings loses about a third of its capacity to the output matrix, and RiLM addresses this overhead through a geodesic decoding approach. The work appeared as a new submission in cs.CL with a cross-listing in cs.AI.

small-models/efficiency/edge-deployment/arxiv