**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).

[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.

**V-trace** is an off-policy actor-critic reinforcement learning algorithm that helps tackle the lag between when actions are generated by the actors and when the learner estimates the gradient. Consider a trajectory $\left(x\_{t}, a\_{t}, r\_{t}\right)^{t=s+n}\_{t=s}$ generated by the actor following some policy $\mu$. We can define the $n$-steps V-trace target for $V\left(x\_{s}\right)$, our value approximation at state $x\_{s}$ as:

$$ v\_{s} = V\left(x\_{s}\right) + \sum^{s+n-1}\_{t=s}\gamma^{t-s}\left(\prod^{t-1}\_{i=s}c\_{i}\right)\delta\_{t}V $$

Where $\delta\_{t}V = \rho\_{t}\left(r\_{t} + \gamma{V}\left(x\_{t+1}\right) - V\left(x\_{t}\right)\right)$ is a temporal difference algorithm for $V$, and $\rho\_{t} = \text{min}\left(\bar{\rho}, \frac{\pi\left(a\_{t}\mid{x\_{t}}\right)}{\mu\left(a\_{t}\mid{x\_{t}}\right)}\right)$ and $c\_{i} = \text{min}\left(\bar{c}, \frac{\pi\left(a\_{t}\mid{x\_{t}}\right)}{\mu\left(a\_{t}\mid{x\_{t}}\right)}\right)$ are truncated importance sampling weights. We assume that the truncation levels are such that $\bar{\rho} \geq \bar{c}$.

V-trace

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

Neural Machine Translation of Rare Words with Subword Units

**GPT-3** is an autoregressive [transformer](https://paperswithcode.com/methods/category/transformers)  model with 175 billion
parameters. It uses the same architecture/model as [GPT-2](https://paperswithcode.com/method/gpt-2), including the modified initialization, pre-normalization, and reversible tokenization, with the exception that GPT-3 uses alternating dense and locally banded sparse attention patterns in the layers of the [transformer](https://paperswithcode.com/method/transformer), similar to the [Sparse Transformer](https://paperswithcode.com/method/sparse-transformer).

Source	Neural Machine Translation of Rare Words with Subword Units
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com