Hassabis said he thinks Google DeepMind helped pioneer RLHF.

The claim is that Google DeepMind (including its predecessor DeepMind) was among those who helped pioneer reinforcement learning from human feedback, not that it was the sole originator. True if DeepMind researchers made foundational early contributions to RLHF, for example as co-authors of the 2017 paper 'Deep reinforcement learning from human preferences' (Christiano et al.) or of other seminal early RLHF work. Mostly true if DeepMind's contribution was real and early but secondary to another lab's. Mixed if DeepMind's early role was minor or disputed. Mostly false if DeepMind's RLHF work came only after the technique was established elsewhere. False if DeepMind had no part in developing RLHF.

Set before the research began.

1. E1 Deep Reinforcement Learning from Human Preferences arXiv (Christiano, Leike, Brown, Martic, Legg, Amodei)

   “Jan Leike DeepMind leike@google.com Tom B Brown nottombrown@gmail.com Miljan Martic DeepMind miljanm@google.com Shane Legg DeepMind legg@google.com”

2. E2 Learning through human feedback DeepMind · 12 June 2017 Own record

   “This research was done as part of an ongoing collaboration between Jan Leike, Miljan Martic, and Shane Legg at DeepMind and Paul Christiano, Dario Amodei, and Tom Brown at OpenAI.”

3. E3 A Tiny History of RLHF | RLHF and Post-Training Book by Nathan Lambert RLHF and Post-Training Book (Nathan Lambert)

   “One of the first papers with an approach similar to modern RLHF was TAMER. TAMER: Training an Agent Manually via Evaluative Reinforcement proposed an approach in which humans iteratively scored an agent’s actions to learn a reward model, which was used to learn the action policy [4].”

4. E4 A Survey of Reinforcement Learning from Human Feedback arXiv

   “Comparisons were first used for direct policy learning (Akrour et al., 2011; Cheng et al., 2011), but were later extended to the reward-learning setting (Wirth et al., 2016; Christiano et al., 2017).”

The 2017 paper 'Deep Reinforcement Learning from Human Preferences' lists three DeepMind-affiliated co-authors: Jan Leike, Miljan Martic and Shane Legg (E1). DeepMind's 2017 blog post describes the work as a collaboration between DeepMind and OpenAI (E2). The criteria name co-authorship of this paper as an example of a foundational early contribution. Earlier preference-based methods existed (E3, E4), but the claim is only that DeepMind helped pioneer RLHF, not that it was the sole originator, so the claim resolves true.