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Found 14 papers, 7 papers with code
Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning
Scaling deep reinforcement learning networks is challenging and often results in degraded performance, yet the root causes of this failure mode remain poorly understood.
The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning
Off-policy deep reinforcement learning (RL) typically leverages replay buffers for reusing past experiences during learning.
Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning
Deep reinforcement learning (RL) agents frequently suffer from neuronal activity loss, which impairs their ability to adapt to new data and learn continually.
Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation
The evaluation of vision-language models (VLMs) has mainly relied on English-language benchmarks, leaving significant gaps in both multilingual and multicultural coverage.
Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training
A training node simultaneously samples data from this buffer based on reward or recency to update the policy using Trajectory Balance (TB), a diversity-seeking RL objective introduced for GFlowNets.
All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages
In pursuit of culturally diverse global multimodal models, our proposed All Languages Matter Benchmark (ALM-bench) represents the largest and most comprehensive effort to date for evaluating LMMs across 100 languages.
Neuroplastic Expansion in Deep Reinforcement Learning
The loss of plasticity in learning agents, analogous to the solidification of neural pathways in biological brains, significantly impedes learning and adaptation in reinforcement learning due to its non-stationary nature.
Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL
The use of deep neural networks in reinforcement learning (RL) often suffers from performance degradation as model size increases.
Mixture of Experts in a Mixture of RL settings
Mixtures of Experts (MoEs) have gained prominence in (self-)supervised learning due to their enhanced inference efficiency, adaptability to distributed training, and modularity.
On the consistency of hyper-parameter selection in value-based deep reinforcement learning
This paper conducts an extensive empirical study focusing on the reliability of hyper-parameter selection for value-based deep reinforcement learning agents, including the introduction of a new score to quantify the consistency and reliability of various hyper-parameters.
In value-based deep reinforcement learning, a pruned network is a good network
Recent work has shown that deep reinforcement learning agents have difficulty in effectively using their network parameters.
Mixtures of Experts Unlock Parameter Scaling for Deep RL
The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance scales proportionally to its size.
Bigger, Better, Faster: Human-level Atari with human-level efficiency
We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark.
Ranked #1 on
Atari Games 100k
on Atari 100k
JaxPruner: A concise library for sparsity research
This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research.
