Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·May 25·8 min read
Goal-Conditioned Agents that Learn Everything All at Once
TL;DR
Goal-conditioned reinforcement learning agents typically discard most observed information, using only transitions relevant to their current goal. Learning Everything all at Once (LEO) enables efficient all-goals learning by jointly outputting values and actions for every possible goal in a single network pass.
✦ Why It Matters
Engineers can now scale all-goals learning to complex environments, extracting more value from collected experience with minimal computational overhead.
Key Takeaways
- Implement LEO to enable efficient all-goals learning without naive relabelling overhead in goal-conditioned RL.
- Use parallel value and action outputs for all goals in single network pass to reduce computation.
- Deploy LEO as teacher network for additional performance gains beyond direct actor usage.
- Expect 250x speedup and improved sample efficiency on complex procedural environments like Craftax.
Full Summary
In goal-conditioned reinforcement learning, agents learn to reach specified target states but waste information by only updating toward commanded goals during training. All-goals learning—using each observed transition to improve performance on every possible goal simultaneously—extracts maximum information but becomes computationally prohibitive with naive relabelling approaches.
Learning Everything all at Once (LEO) solves this by redesigning the neural network to output value estimates and action predictions for all goals in parallel within a single forward pass. Tested on Craftax (a complex procedural environment) and continuous control tasks, LEO significantly outperforms alternatives while delivering 250x computational speedup versus relabelling baselines.
The method further improves by using LEO as a teacher network rather than direct actor. Open-sourced implementation enables practical adoption in complex environments.