Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Supervised Seeded Iterated Learning for Interactive Language Learning   Language drift has been one of the major obstacles to train language models
through interaction. When word-based conversational agents are trained towards
completing a task, they tend to invent their language rather than leveraging
natural language. In recent literature, two general methods partially counter
this phenomenon: Supervised Selfplay (S2P) and Seeded Iterated Learning (SIL).
While S2P jointly trains interactive and supervised losses to counter the
drift, SIL changes the training dynamics to prevent language drift from
occurring. In this paper, we first highlight their respective weaknesses, i.e.,
late-stage training collapses and higher negative likelihood when evaluated on
human corpus. Given these observations, we introduce Supervised Seeded Iterated
Learning to combine both methods to minimize their respective weaknesses. We
then show the effectiveness of \algo in the language-drift translation game.

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The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
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The categories of this paper are: supr-con
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The categories of this paper are: alg-geom
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The categories of this paper are: stat
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess