Focusing on empirical results:

Learning to summarize  from human feedback was good, for several reasons.

I liked the recent paper empirically demonstrating objective robustness failures hypothesized in earlier theoretical work on inner alignment.

 

If we take "tangible" to mean executable:

• A primitive prototype and a framework for safety via debate (2018-9). Bit quiet since.
• Carey's 2019 proof of concept / extension of quantilizers
• Stiennon et al (2020) is an extremely encouraging example of a large negative "alignment tax" (making it safer also made it work better)
   

But as Kurt Lewin once said "there's nothing so practical as a good theory". In particular, theory scales automatically and conceptual work can stop us from wasting effort on the wrong things.

• CAIS (2019) pivots away from the classic agentic model, maybe for the better
• The search for mesa-optimisers (2019) is a step forward from previous muddled thoughts on optimisation, and they make predictions we can test them on soon.
• The Armstrong/Shah discussion of value learning changed my research direction for the better.

Also Everitt et al (2019) is both: a theoretical advance with good software.