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On 4 April 2025 Salesforce posted APIGen-MT, a two-phase pipeline for generating multi-turn agent training data, and released the xLAM-2-fc-r model series, from 1B to 70B parameters, trained on that data.
A February 2024 paper introduced DeepSeekMath 7B, a math-focused language model, and with it Group Relative Policy Optimization, a reinforcement-learning method that drops PPO's critic model.
On 17 January 2024 Google DeepMind announced AlphaGeometry, a system that pairs a language model with a symbolic deduction engine and solved 25 of 30 olympiad geometry problems in a benchmark, close to the 25.9 averaged by human gold medallists.
FunSearch, published in Nature in December 2023, used large language models for program search rather than direct solution search, and found results better than the previous best on the cap set problem and online bin packing.
On 3 April 2023 EleutherAI released Pythia, 16 models from 70M to 12B parameters trained on the same public data in the same order, with 154 public checkpoints each, built for studying how capabilities develop across training and scale.
In October 2022 Tsinghua University's KEG group and Zhipu AI published GLM-130B, an open bilingual model with 130 billion parameters that was accepted to ICLR 2023.
On 4 April 2022, Google Research announced PaLM, a 540-billion-parameter language model trained on 6,144 TPU v4 chips with the Pathways system, and reported strong multi-step reasoning and above-average-human results on BIG-bench.
In July 2021 the journal Nature published the AlphaFold2 paper from DeepMind. The AI system predicts a protein's three-dimensional shape from its amino-acid sequence, and DeepMind then released its code and a free structure database.
On 31 December 2020 EleutherAI released The Pile, an openly available 825 GiB English training corpus assembled from 22 smaller datasets, with a paper showing models trained on it beat both comparison corpora on every component.
On 19 November 2019, DeepMind posted a MuZero preprint to arXiv: it plans with a learned model rather than rules, matched AlphaZero in Go, chess and shogi, and set a state of the art on 57 Atari games.
On 11 October 2018 Google researchers posted the BERT paper to arXiv. BERT uses context from both directions in every layer, and a single added output layer is enough to adapt it to tasks such as question answering.
A preprint posted to arXiv by DeepMind on 5 December 2017 described one general reinforcement-learning algorithm that, given only the rules, learned chess, shogi and Go by self-play and reached superhuman level in all three within 24 hours.
In 2017, DeepMind announced AlphaGo Zero, a Go system that started from random play, learned only by self-play, and outperformed earlier AlphaGo versions with one neural network and far less hardware.
On 27 January 2016 DeepMind announced AlphaGo alongside a paper in Nature reporting that the program had beaten European Go champion Fan Hui 5–0 in October 2015 — the first time a computer program had beaten a professional Go player.
