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A real masterclass in this week’s #MSMRL - where Iñaki de la Parra & I sit down with world-class coach Joel Filliol on the biggest mistake in endurance training right now: “Zone 2 has started to creep up.” “What used to be easy training… isn’t really easy anymore.” “The... show more

Alan Couzens

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17,138 views • 3 months ago •via X (Twitter)

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A couple of months back, I wrote a post: “Strength Training for Endurance Athletes -You’re Doing It Wrong.” It sparked… a bit of discussion. And argument. So for this #MSMRL, Iñaki de la Parra & I brought in some serious backup: Erin Carson - strength coach to Ironman champions, Olympic medalists, and some of the most durable endurance athletes on the planet. Her message? Most “strength training” for endurance athletes just looks like all their other training - adding fatigue & furthering imbalances. And that’s exactly what it shouldn’t be! If you care about actually getting functionally stronger, more durable, and faster (not just more tired!), this one’s worth your time.

A couple of months back, I wrote a post: “Strength Training for Endurance Athletes -You’re Doing It Wrong.” It sparked… a bit of discussion. And argument. So for this #MSMRL, Iñaki de la Parra & I brought in some serious backup: Erin Carson - strength coach to Ironman champions, Olympic medalists, and some of the most durable endurance athletes on the planet. Her message? Most “strength training” for endurance athletes just looks like all their other training - adding fatigue & furthering imbalances. And that’s exactly what it shouldn’t be! If you care about actually getting functionally stronger, more durable, and faster (not just more tired!), this one’s worth your time.

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"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"

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