I’ve been building RL‑style agents at the NVIDIA DGX Hackathon and my job for a while, and I keep hitting the same wall: everyone wants agents that learn, almost nobody can afford real RL.
That's when I learned something crazy:
-> Frontier training costs have grown about 2.4×/year since 2016, with single runs in the $30–40M range and projections crossing $1B this decade.
And what ends up happening is most agents in production end up replaying prompts and tools over a growing context window instead of improving reasoning capabilities.
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My bet is that we’re aiming learning at the wrong target. A lot of the leverage is in how agents remember, not in constantly retraining the full policy. And with recent innovations in mem-alpha which treats memory construction as an RL problem and uses a small controller to maintain it, I have began to see a solution.
Instead of constantly retraining policies, I’m working on Cadenza v1.1—a mem‑alpha–style memory layer that learns how the agent remembers (what to keep, link, compress, forget) while leaving the base model mostly fixed.
This can create RL‑grade specialization, but it's driven by a small memory controller rather than huge PPO runs.
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I’m looking for a few teams who are:
-> Running agents in production or serious pilots.
-> Feeling the “no real learning / RL too expensive” pain.
If this direction resonates—shifting learning into the memory layer rather than the policy—I’d love feedback or to talk through your use case.
aparekh02•1h ago
That's when I learned something crazy: -> Frontier training costs have grown about 2.4×/year since 2016, with single runs in the $30–40M range and projections crossing $1B this decade.
And what ends up happening is most agents in production end up replaying prompts and tools over a growing context window instead of improving reasoning capabilities.
===
My bet is that we’re aiming learning at the wrong target. A lot of the leverage is in how agents remember, not in constantly retraining the full policy. And with recent innovations in mem-alpha which treats memory construction as an RL problem and uses a small controller to maintain it, I have began to see a solution.
Instead of constantly retraining policies, I’m working on Cadenza v1.1—a mem‑alpha–style memory layer that learns how the agent remembers (what to keep, link, compress, forget) while leaving the base model mostly fixed.
This can create RL‑grade specialization, but it's driven by a small memory controller rather than huge PPO runs.
===
I’m looking for a few teams who are:
-> Running agents in production or serious pilots.
-> Feeling the “no real learning / RL too expensive” pain.
If this direction resonates—shifting learning into the memory layer rather than the policy—I’d love feedback or to talk through your use case.