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arxiv: 2605.23551 · v1 · pith:ZEP54EVZnew · submitted 2026-05-22 · 💻 cs.LG · cs.AI

Goal-Conditioned Agents that Learn Everything All at Once

Michael Matthews , Matthew Jackson , Michael Beukman , Thomas Foster , Alistair Letcher , Scott Fujimoto , C\'edric Colas , Jakob Foerster This is my paper

Pith reviewed 2026-05-25 04:56 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords goal-conditioned reinforcement learningall-goals learningoff-policy updatesLEOCraftaxcontinuous control
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The pith

Goal-conditioned RL agents can learn from every possible goal in a single network pass instead of relabelling each transition.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a method called LEO that lets a goal-conditioned agent output values and actions for all goals simultaneously. This replaces the usual expensive process of relabelling each transition for every goal. The resulting updates are far cheaper yet still use every piece of experience, producing stronger performance on Craftax and matching baselines on continuous control tasks. The same network can also serve as a teacher to train a separate actor. If the approach scales, it removes a major computational barrier that has kept all-goals learning out of reach for complex environments.

Core claim

A network that jointly produces value estimates and actions for every goal in one forward pass enables efficient, parallel all-goals off-policy updates. This removes the need for naive relabelling while still extracting information about every goal from each transition, yielding more than 250 times faster training than relabelling methods, higher returns on goal-conditioned Craftax, and competitive results on continuous control benchmarks.

What carries the argument

The LEO network architecture that produces goal-conditioned value and action outputs for the entire goal set in a single forward pass.

If this is right

  • All-goals learning becomes practical at the scale of environments like Craftax without prohibitive compute cost.
  • Each trajectory yields useful training signals for every achievable goal rather than only the commanded one.
  • The same joint-output network can be distilled into a separate actor for additional performance gains.
  • Training time on goal-conditioned tasks drops by more than two orders of magnitude relative to relabelling.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same joint-output trick could be applied to multi-task RL by treating each task as a pseudo-goal.
  • Environments with continuous goal spaces might become tractable if the output layer is replaced by a parametric goal encoder.
  • LEO-style networks could reduce the sample complexity gap between goal-conditioned and standard single-goal RL.

Load-bearing premise

Jointly outputting values and actions for every goal at once stays computationally and numerically stable without loss of accuracy as the number of goals or environment complexity increases.

What would settle it

Train an LEO agent on a goal-conditioned task with several thousand distinct goals and measure whether wall-clock time per update remains more than 100 times faster than relabelling while final task performance stays at or above the reported baselines.

Figures

Figures reproduced from arXiv: 2605.23551 by Alistair Letcher, C\'edric Colas, Jakob Foerster, Matthew Jackson, Michael Beukman, Michael Matthews, Scott Fujimoto, Thomas Foster.

Figure 1
Figure 1. Figure 1: We consider with respect to what goal a given transition in a trajectory is updated to in different GCRL paradigms. In vanilla GCRL (1a), the update is done with respect to the goal that was commanded, even if this goal is never satisfied. When using HER (1b), the trajectory is relabelled with a goal that was achieved later on, providing a positive signal. With LEO (1c), we propose updating jointly with re… view at source ↗
Figure 2
Figure 2. Figure 2: Speed comparison of different methods on the CraftaxGC benchmark with a goal set of size 512. We see that LEO learns with respect to the entire goal set with only a 34% slowdown compared to regular single goal learning. This is in contrast to na¨ıve all-goals relabelling, which grows each batch of trajectories by a factor of 512, resulting in 264× slower throughput than LEO. All methods here use PQN as the… view at source ↗
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Mean success rate across all goals on CraftaxGC. The shaded area denotes 1 standard error over 5 seeds. We see LEO outperforming UVFA-style baselines on the larger goal set, but not on the smaller one. Dual LEO performs well in both cases, with the PPO variant achieving the best final performance in both settings. We then add losses to push the PPO policy π(s, g) to￾wards the greedy LEO policy argmaxaQLEO(… view at source ↗
Figure 5
Figure 5. Figure 5: Mean success rate over selected goals on CraftaxGC. The shaded area denotes 1 standard error over 5 seeds. LEO performs well on hard goals (top row) but can underperform on easy goals (bottom row), due to the late fusion issue. Dual LEO resolves this problem, achieving strong results on the hard goals without sacrificing performance on easy goals. agent to take one path in the game (“grow a plant”) or a wi… view at source ↗
Figure 6
Figure 6. Figure 6: Mean success rate for the inventory/coal-1 goal for Dual LEO (PQN), when acting greedily with respect to each of its components. The shaded area denotes 1 standard error over 5 seeds. Validating our hypothesis in Section 3.2, we see that the LEO network learns to achieve the goal early, providing positive examples of goal completion that allows the UVFA network to learn on. Craftax On the full Craftax benc… view at source ↗
Figure 7
Figure 7. Figure 7: shows that SAC+LEO outperforms all baselines on the smaller U Maze. On the larger maze the results are less clear, with LEO, SAC+HER and CRL all performing simi￾larly. We also investigated using a Dual LEO critic for SAC, but found it did not noticeably affect performance. This could be because the main difficulty in the ant maze tasks is learning the locomotive gait, rather than the differing goal positio… view at source ↗
Figure 8
Figure 8. Figure 8: Mean success rates for all algorithms on CraftaxGC. Shaded area denotes 1 standard error over 5 seeds. Part 1. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Mean success rates for all algorithms on CraftaxGC. Shaded area denotes 1 standard error over 5 seeds. Part 2. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Mean success rates for all algorithms on CraftaxGC. Shaded area denotes 1 standard error over 5 seeds. Part 3. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Mean success rates for all algorithms on CraftaxGC. Shaded area denotes 1 standard error over 5 seeds. Part 4. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Mean success rates for all algorithms on CraftaxGC. Shaded area denotes 1 standard error over 5 seeds. Part 5. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Mean success rates for all algorithms on CraftaxGC. Shaded area denotes 1 standard error over 5 seeds. Part 6. 27 [PITH_FULL_IMAGE:figures/full_fig_p027_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Mean success rates for all algorithms on CraftaxGC. Shaded area denotes 1 standard error over 5 seeds. Part 7. 28 [PITH_FULL_IMAGE:figures/full_fig_p028_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Mean success rates for all algorithms on CraftaxGC for Craftax-Classic. Shaded area denotes 1 standard error over 5 seeds. Part 1. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Mean success rates for all algorithms on CraftaxGC for Craftax-Classic. Shaded area denotes 1 standard error over 5 seeds. Part 2. 30 [PITH_FULL_IMAGE:figures/full_fig_p030_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: shows the results on CraftaxGC when sampling uniformly from the goal distribution, rather than only from previously observed goals. This makes little difference on Craftax-Classic, but makes all methods perform significantly worse on Craftax. LEO and Dual LEO still perform well, with the gap between them and the baselines being even bigger, showing that they are more resilient to the difficulty of the goa… view at source ↗
Figure 18
Figure 18. Figure 18: Results on CraftaxGC with subsampled goal sets after training for 1 billion timesteps, averaged over all goals. Shaded area denotes 1 standard error over 5 seeds. 100 200 300 400 Goal Set Size 0.2 0.4 0.6 0.8 1.0 Success Rate tools/stone-pickaxe Algorithm PPO LEO [PITH_FULL_IMAGE:figures/full_fig_p035_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Results on CraftaxGC with subsampled goal sets after training for 1 billion timesteps for the tools/stone-pickaxe goal. Shaded area denotes 1 standard error over 5 seeds. 35 [PITH_FULL_IMAGE:figures/full_fig_p035_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Per-goal results for Dual LEO (PQN) when acting greedily with respect to each of its components on Craftax-Classic at 50 million timesteps. Part 1. block-map/path-left block-map/path-right block-map/path-down block-map/path-up inventory/wood-9 inventory/stone-2 inventory/sapling-8 inventory/stone-3 inventory/stone-4 all block-map/furnace-right block-map/furnace-up block-map/furnace-left block-map/furnace-… view at source ↗
Figure 21
Figure 21. Figure 21: Per-goal results for Dual LEO (PQN) when acting greedily with respect to each of its components on Craftax-Classic at 50 million timesteps. Part 2. 36 [PITH_FULL_IMAGE:figures/full_fig_p036_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Mean goal success rate at 1 billion timesteps on Craftax for LEO and Dual LEO (PQN) when updating with respect to only a random subset of goal heads. For Dual LEO we only modify the LEO update. The shaded area denotes standard error over 4 seeds. 37 [PITH_FULL_IMAGE:figures/full_fig_p037_22.png] view at source ↗
read the original abstract

A goal-conditioned reinforcement learning agent exploring an environment will see a wealth of information throughout a trajectory, most of which is discarded when only performing on-policy updates with respect to the commanded goal. All-goals learning, where each transition is used for learning off-policy with respect to every goal, allows agents to extract maximal information, however it is usually computationally infeasible when done via naive relabelling. This can be overcome by jointly outputting values and actions for every goal at once, allowing for efficient, parallel all-goals updates with a single pass through the network, in a process we call Learning Everything all at Once (LEO). We show that this approach significantly outperforms other methods on goal-conditioned Craftax and is competitive with existing baselines on continuous control environments, while achieving a >250x speed-up compared to all-goals relabelling. We then go on to show that this approach can be made even more powerful by using LEO as a teacher network, rather than a direct actor. We hope that, by unlocking all-goals learning at scale, LEO can serve as a useful tool for RL practitioners in complex environments. We open source our code.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces Learning Everything all at Once (LEO), a goal-conditioned RL method that replaces naive all-goals relabelling with a single forward pass through a network whose output head jointly predicts values and actions for every goal. The central claim is that this yields >250× wall-clock speedup while matching or exceeding the performance of all-goals relabelling, with reported gains on goal-conditioned Craftax and competitive results on continuous-control suites. An additional teacher-student variant is also presented.

Significance. If the efficiency and accuracy claims hold under scaling, LEO would make all-goals learning practical in environments where |G| is large, directly improving sample efficiency without extra passes. The open-sourced code is a concrete strength that supports reproducibility and follow-up work.

major comments (2)
  1. [§3] §3 (Method), joint-output architecture: the paper asserts equivalence to all-goals relabelling but provides no capacity or interference analysis showing that a fixed-size output head preserves per-goal accuracy once |G| exceeds the Craftax scale used in experiments. This directly bears on whether the reported speedup and performance gains survive larger goal sets.
  2. [§4] §4 (Experiments), Craftax and continuous-control tables: results are reported for a fixed goal cardinality; no ablation varies |G| or goal dimensionality while measuring value-error or policy degradation relative to the relabelling baseline. Without this, the claim that joint prediction incurs “no accuracy loss” remains untested at the scales where the computational advantage would matter most.
minor comments (2)
  1. [Abstract] Abstract and §1: performance and speedup numbers are stated without reference to the exact baselines or number of seeds; adding these citations would improve immediate readability.
  2. [§2] Notation: the distinction between the commanded goal g and the full goal set G is occasionally ambiguous in the equations; a short clarifying sentence in §2 would help.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review. We address each major comment below, focusing on the technical points raised regarding the joint-output architecture and experimental validation.

read point-by-point responses

  1. Referee: [§3] §3 (Method), joint-output architecture: the paper asserts equivalence to all-goals relabelling but provides no capacity or interference analysis showing that a fixed-size output head preserves per-goal accuracy once |G| exceeds the Craftax scale used in experiments. This directly bears on whether the reported speedup and performance gains survive larger goal sets.

    Authors: The LEO architecture produces identical per-goal value and policy outputs to naive relabelling by expanding the final layer to a joint head whose size scales linearly with |G|, while requiring only a single forward pass through the shared backbone. On the Craftax goal cardinalities tested, this yields performance matching or exceeding the relabelling baseline, indicating that any representational interference is not detrimental at those scales. We agree that an explicit capacity analysis for substantially larger |G| is absent from the current manuscript and will add a dedicated paragraph in §3 of the revision discussing output-head scaling and potential interference, together with a brief complexity argument. revision: partial

  2. Referee: [§4] §4 (Experiments), Craftax and continuous-control tables: results are reported for a fixed goal cardinality; no ablation varies |G| or goal dimensionality while measuring value-error or policy degradation relative to the relabelling baseline. Without this, the claim that joint prediction incurs “no accuracy loss” remains untested at the scales where the computational advantage would matter most.

    Authors: We acknowledge that the reported experiments use the fixed goal sets native to each environment and do not include an explicit sweep over |G|. The >250× wall-clock speedup arises precisely because forward-pass cost is independent of |G|, while relabelling cost grows linearly. To address the concern, we will add an appendix ablation that varies |G| on a controlled synthetic goal-conditioned task, reporting both value error and policy performance relative to the relabelling baseline. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical engineering method with no self-referential derivations

full rationale

The paper describes LEO as a practical implementation of joint value/action heads for all-goals updates in goal-conditioned RL, with performance claims based on empirical results on Craftax and continuous control tasks. No equations, derivations, or load-bearing self-citations appear in the provided text that would reduce any claimed prediction or result to a fitted quantity or prior author work by construction. The approach is presented as an efficiency technique built on standard components, making the derivation chain self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no mathematical formulation, so no free parameters, axioms, or invented entities can be identified.

pith-pipeline@v0.9.0 · 5755 in / 1050 out tokens · 18386 ms · 2026-05-25T04:56:23.027133+00:00 · methodology

discussion (0)

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Reference graph

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