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Researchers Develop Method to Estimate Value of Computation in RL Agents

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💡Classic work on POMDPs tells us how, when agents have limited perception, to compute the "value of information" in each observation. Our work tells us how to estimate a the "value of computation".

There's a rich interplay between reward, information, and computation!

Ben Eysenbach@ben_eysenbach

🏗️One actionable takeaway: a new "latent-reasoning" policy architecture (IRU) that is a drop-in replacement for your current RL policy.

Code snippet: https://github.com/RajGhugare19/on-computation-and-rl/blob/main/ogbench/impls/utils/networks.py#L808

6:44 PM · Jun 16, 2026 · 294 Views

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Ben Eysenbach@ben_eysenbach

🏗️One actionable takeaway: a new "latent-reasoning" policy architecture (IRU) that is a drop-in replacement for your current RL policy.

Code snippet: https://github.com/RajGhugare19/on-computation-and-rl/blob/main/ogbench/impls/utils/networks.py#L808

Ben Eysenbach@ben_eysenbach

@GhugareRaj 's new paper shows how inference-time compute allows RL policies (trained from scratch!) to solve more tasks (theoretically + empirically).

5h21800

Ben Eysenbach@ben_eysenbach

🧠 One more future direction: new models of bounded rationality (cf maximum entropy methods) that more realistically model the computational constraints of natural and artificial agents.

Reach out if interested in collaborating on future directions!

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