I would define sentience as an awareness of oneself in relation to one's environment, that generalizes to different aspects of self and environment (eg. social, physical etc)
One of the key aspects of intelligence is learning from 3rd-person perspectives - eg. you observe another ape hunting with a stick, and decide to emulate that behavior for yourself. This seemingly simple behavior requires a lot of work, you have to recognize other apes as agents similar to yourself, then map their actions to your own body, then perform those actions and see if it had the intended outcome.
Current RL agents are not capable of this. The data used for GATO and similar agents for imitation learning are in first person perspective. If an agent could learn from a 3rd person perspective, via direct observations of its environment, I would say that would be the beginning of machine sentience.
I think the main three obstacles in our way are imbuing models with (from least to most difficult):
1. continuous execution, which would be necessary for a real-time "stream of consciousness"
2. discreet short and long term memory and the ability to use it productively
3. plasticity at runtime
1 is just a matter of finding more efficient architectures and cheapening compute, both fronts we're constantly making progress on. 2 is a little more muddy, but there are already some really impressive models that have retrieval mechanisms which allow them to search through databases to help them contextualize prompts which I believe is a step in the right direction. 3 is probably the hardest in my opinion--I think we need several fundamental breakthrough discoveries before our models are anywhere near as plastic as, say, a human brain. My hypothesis is that exceptional plasticity is necessary for sentience, but obviously that's just my own personal opinion.
Anyways, thought I'd chime in with my two cents. I'd love to hear others' thoughts on the feasibility/infeasibility of near-term AGI.