Evgenii Nikishin

I am a researcher at OpenAI, working on improving the capabilities of language models to solve scientific problems.

I received a PhD in Computer Science from Mila, University of Montreal advised by Pierre-Luc Bacon and Aaron Courville. Before that, I studied at Cornell University, Higher School of Economics, and Lomonosov Moscow State University. During my studies, I interned with the RL team at DeepMind and at ETH Zürich.

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We show that neuron saturation, which traditionally was viewed as undesirable, could instead lead to sparse yet accurate networks.

We demonstrate that individual ensemble members in RL exhibit surprisingly low performance (whilst aggregate returns are adequate) and propose a remedy.

We propose an intervention for diagnosing the loss of plasticity phenomenon in RL and dynamically growing neural networks in RL for increasing computational efficiency.

An analysis of plasticity loss showing its relation to pathological loss landscapes and demonstrating efficiency of layer normalization to mitigate it.

Resets unlock increasing sample efficiency by scaling the number of updates per environment step.

We identify a damaging tendency of deep RL agents to overfit early experiences and propose a simple yet powerful remedy based on periodic resetting of a part of the agent.

A model learning method for RL that directly optimizes the sum of rewards instead of likelihood, a proxy to the agent's objective.

We propose an algorithm to construct discrete token adversarial examples based on the notion of synonyms.

Domain adaptation via learning shared dynamics in a latent space with adversarial matching of latent states.

Averaging weights during training of an RL agent stabilizes the achieved performance.