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Evaluate a long-horizon agent by testing a specific capability under a declared protocol—not by reading one leaderboard number. Define whether you are measuring learning efficiency, final task performance, generalization, reliability, coordination, or operating cost; choose tasks and held-out splits that expose that capability; report both terminal outcomes and meaningful progress; then publish enough implementation detail for another lab to reproduce the comparison.
Start with the capability claim
“Long-horizon agent” describes a task structure, not a single capability. An agent may need to make dozens of sequential decisions, recover from mistakes, use tools, coordinate with other agents, or learn from limited interaction. A benchmark that answers one of those questions may be poorly suited to another.
Write the claim as a testable sentence before selecting an environment. Examples include:
- Learning efficiency: “This policy reaches a target performance level with fewer environment interactions.”
- Final performance: “This agent completes tasks reliably under the stated task distribution.”
- Generalization: “This agent transfers from training conditions to unseen levels, tasks, or variants.”
- Long-horizon reliability: “This agent maintains progress over a long sequence and fails less often as the horizon grows.”
- Coordination: “This multi-agent system improves outcomes through useful cooperation rather than merely adding more agents.”
- Operational efficiency: “This system achieves a given outcome with fewer steps, tool calls, tokens, runtime, or monetary cost.”
These claims require different tasks and metrics. A benchmark designed for sample efficiency and visual generalization is not automatically evidence about a language agent completing a web workflow. Likewise, a computer-use benchmark is not a substitute for a controlled reinforcement-learning comparison.
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Choose tasks and splits that expose the challenge
Use a task family with enough variation to reveal the difficulty you claim to measure. Document which tasks, levels, or variants are used for training, tuning, validation, and final evaluation. If generalization is part of the claim, keep the evaluation set genuinely held out and report it separately from training-condition performance.
Match benchmark scope to the question
| Capability being tested | Suitable evidence | What to report | Important limitation |
|---|---|---|---|
| Sample efficiency and generalization in reinforcement learning | Procgen, whose NeurIPS 2020 competition benchmark standardized end-to-end training and rollout evaluation | Performance against interaction or training budget, plus separate results on training and unseen levels | It does not test every language-agent or real-world workflow capability |
| Long-horizon language-agent policy learning | ALFWorld, WebShop, and ScienceWorld, as used by BEACON | Task split, environment version, terminal success, milestone or segment progress, and training efficiency | These three environments do not exhaust the space of long-horizon tasks |
| Computer-use workflows | OSWorld 2.0 | Workflow completion, partial score, step or tool-call budget, and the exact model and configuration | It is an adjacent computer-use evaluation, not a replacement for an RL benchmark |
| Multi-agent collaboration | AgentWorld | Binary task success and a collaboration-specific measure such as Causal Collaboration Effectiveness | Results apply to its multi-agent sandbox and task construction |
| Web-agent simulation breadth | WebWorld and WebWorld-Bench | The dimensions evaluated, interaction length, and simulator assumptions | Its breadth does not by itself establish that one RL protocol is superior |
Do not merge scores from these rows into a single ranking. They measure different task distributions, agent classes, and experimental questions.
Keep training and evaluation conditions separate
A credible split prevents hidden adaptation. State whether an agent saw the evaluation levels, task templates, websites, demonstrations, or tool outcomes during training or prompt tuning. For procedural or procedurally generated environments, report the generation and seed policy. For language-agent tasks, name the exact task split and whether failed trajectories were reused for training.
Procgen is a useful precedent because its design makes sample efficiency and generalization explicit rather than treating a single in-distribution score as the entire result. The authors note that generalization remains a fundamental challenge and that benchmarks should measure it directly.
Define outcomes that reveal both success and learning
Choose a primary outcome that has a defensible relationship to the capability claim. Binary completion is appropriate when a task has a verifiable finish condition, but it can hide whether an agent made useful progress before the final failure. Conversely, a shaped score can reward behavior that looks productive without accomplishing the task.
Core reporting axes
- Outcome: binary completion, environment reward, or a validated partial-progress score.
- Learning efficiency: interactions, episodes, training tokens, or another budget required to reach a stated performance level.
- Generalization: results on held-out tasks, levels, environments, or task types.
- Horizon and reliability: task length, completion frequency, failure modes, and variation across independent runs.
- Operational cost: environment steps, tool calls, runtime, model calls, tokens, or money, measured consistently.
- Attribution: whether the metric records only the terminal result or identifies meaningful subgoal progress.
These are selectable axes, not a universal scorecard. A short, verifiable task may need little beyond completion and cost. A sparse-reward workflow may require milestones, failure localization, and a learning curve.
Plot learning curves, not only end points
For a training comparison, show performance as a function of interaction or training budget. Include the budget at which each curve reaches a target level, not only the final value after a chosen number of updates. Two methods can finish at the same success rate while one learns much faster; conversely, a method can have a higher final score while requiring substantially more interaction.
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Use the same evaluation schedule, rollout limit, task set, and aggregation rule for every method. If evaluation itself consumes interaction, state whether those episodes count against the reported training budget.
Preserve partial progress in sparse-reward tasks
Long-horizon reinforcement learning creates a credit-assignment problem: an early action may be correct, yet the trajectory receives a failure signal because a later step went wrong. Sparse successful trajectories also make learning sample-inefficient. A terminal success rate alone cannot show whether the policy is learning useful subskills.
Milestones and segment-level attribution
BEACON, described by Wang and coauthors at ICML 2026, addresses these issues by partitioning trajectories at milestone boundaries, shaping rewards within segments, and estimating advantages at both trajectory and segment scales. The intended effect is to preserve information about partial completion while retaining the terminal objective.
The implementation identifies environment-defined milestone signals: trial_id for ALFWorld, milestone_achieved for WebShop, and subgoal_completed for ScienceWorld. These signals are environment-specific; do not assume that a milestone in one environment has the same meaning in another. Report the exact signal, segmentation rule, and reward transformation so readers can determine whether the measure reflects real progress.
How to report milestone metrics
- Define each milestone operationally, including the event that marks it complete.
- State whether milestones are ordered, optional, repeatable, or prerequisite-dependent.
- Report the fraction of trajectories reaching each milestone, not only the average count if order matters.
- Show terminal success alongside milestone statistics; do not replace the primary outcome with a shaped proxy.
- Explain how milestone rewards affect training and whether they are used during evaluation.
In its reported ALFWorld long-task setting, BEACON achieved 92.9% success versus 53.5% for GRPO, with effective sample utilization of 82.0% versus 23.7%. These are Wang et al.’s 2026 results under that paper’s implementation, task split, and training protocol—not general baselines for every ALFWorld or GRPO reproduction.
Make protocol details part of the result
Performance comparisons are only interpretable when their conditions are aligned. Jordan, Chandak, Cohen, Zhang, and Thomas wrote in their 2020 evaluation-methodology paper that “Performance evaluations are critical for quantifying algorithmic advances in reinforcement learning.” Their central practical concern is that flawed or underspecified metrics produce inconsistent, difficult-to-replicate conclusions.
Minimum protocol record
- Environment: name, release or commit, task generator, and relevant configuration.
- Task composition: number and type of tasks, levels, variants, and any filtering.
- Splits: training, validation, test, and held-out generalization conditions.
- Agent: policy or model version, architecture, prompting or inference settings, tools, and memory configuration.
- Training: data sources, interaction budget, update schedule, optimizer settings relevant to interpretation, and checkpoint-selection rule.
- Rollouts: maximum steps, retries, parallelism, temperature or sampling policy, and termination behavior.
- Scoring: reward implementation, success predicate, partial-score formula, and handling of invalid or timed-out episodes.
- Replication: number of independent runs or random seeds, aggregation method, and uncertainty or run-to-run variation.
Keep implementation instructions tied to the version you used. The BEACON repository lists separate environment setup requirements, including a Python version of 3.10 or earlier for its WebShop setup; dependencies can change, so current users should consult the repository’s own instructions rather than treating that requirement as timeless.
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Report uncertainty and variation
At minimum, provide the number of runs and the aggregation rule. Where available, show confidence intervals, standard deviations, or per-seed values. A two-point difference from one seed is not the same evidence as a stable difference across many independent runs. If computational limits force a small number of seeds, state that limitation and avoid strong claims.
Understand what adjacent benchmarks can and cannot tell you
OSWorld 2.0: long-horizon computer use
OSWorld 2.0 describes 108 computer-use workflows spanning everyday and professional tasks. Its project page reports that 69.6% of tasks take skilled human users more than one hour, with a median human completion time of about 1.6 hours. In the named Claude Opus 4.7 setting, tasks required an average of 318 tool calls.
The same project reports 20.6% binary completion and a 54.8% partial score for Claude Opus 4.8 at a 500-step primary binary-completion metric, using the page’s stated maximum-thinking and batched-tool-call configuration. Keep each number attached to that benchmark, model, metric, and configuration. These figures illustrate the difficulty of long computer workflows; they are not head-to-head RL results against BEACON or Procgen.
AgentWorld: multi-agent collaboration
The September 2026 AgentWorld preprint presents 100 human-annotated tasks and 100 augmented variants. Tasks require 3–20 agents to coordinate over more than 50 interaction rounds in an MMORPG sandbox. The authors introduce Causal Collaboration Effectiveness alongside binary task success and report 52.0% task success for the best tested model.
This setting is useful when the claim concerns cooperation, attribution, or the value of additional agents. It should not be interpreted as a universal measure of single-agent long-horizon competence.
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WebWorld and its WebWorld-Bench cover nine evaluation dimensions in a large-scale simulated web world, with interactions exceeding 30 steps. Use this kind of benchmark to examine breadth of web-agent behavior and simulator coverage. Its scope does not prove that one reward design, training algorithm, or RL evaluation protocol is best for every web task.
Run an evaluation as a controlled experiment
- Write the claim and primary metric. Specify the agent class, task family, capability, and threshold or comparison that would support the claim.
- Freeze the benchmark definition. Record the environment version, task generator, splits, scoring code, and rollout limits before running final evaluations.
- Separate tuning from testing. Use validation tasks for prompt, hyperparameter, and checkpoint decisions; keep the test set untouched until the final run.
- Set a budget. Declare interaction, training, step, tool-call, token, runtime, or monetary limits and apply them equally to compared systems.
- Instrument progress. Log terminal outcomes, milestone events, subgoal completion, retries, invalid actions, and failure causes where the environment permits.
- Run independent repetitions. Vary random seeds or other declared sources of stochasticity and preserve per-run records.
- Evaluate held-out conditions. If generalization is claimed, run unseen levels, task variants, or task types and report them in a separate row or figure.
- Publish the analysis boundary. State which environments and conditions the result covers and which broader capabilities it does not establish.
Common evaluation mistakes and their fixes
Using one benchmark as a universal test
Failure: Treating a Procgen score, language-agent completion rate, or computer-use result as a general measure of long-horizon intelligence.
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Fix: Tie every conclusion to the benchmark’s agent class, task distribution, horizon, and protocol. Use complementary evaluations when the capability claim spans multiple settings.
Reporting only terminal success
Failure: A failed final step makes a trajectory with substantial correct progress indistinguishable from one that failed immediately.
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Fix: Add verified milestones or partial-progress measures, document their scoring, and retain terminal success as the primary outcome when completion is the actual goal.
Confusing final performance with learning efficiency
Failure: Declaring a method more efficient because it has a higher score after an unspecified or unequal training budget.
Fix: Plot performance against interaction or training budget and report the budget needed to reach defined levels.
Leaking evaluation tasks into tuning
Failure: Selecting prompts, checkpoints, or hyperparameters after inspecting test tasks, then calling the result generalization.
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Comparing mismatched configurations
Failure: Comparing scores obtained with different model versions, step limits, retries, task splits, seeds, or success predicates.
Fix: Align the protocol or label the comparison as cross-setting context rather than an algorithmic head-to-head.
Hiding variance
Failure: Presenting a single seed or mean without the number of runs.
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Fix: Report independent runs, aggregation, and uncertainty; preserve per-run results when feasible.
Interpret scores narrowly
A benchmark score is an estimate of performance under a particular task distribution and evaluation procedure. It supports a bounded statement such as “the agent completed this fraction of held-out WebShop tasks under the declared rollout limit,” not “the agent has solved long-horizon reasoning.”
When a result is strong, ask which component produced the gain: better exploration, more efficient credit assignment, stronger language modeling, additional retries, a larger interaction budget, or a changed success predicate. When a result is weak, inspect milestone and failure data before concluding that the entire approach lacks long-horizon ability.
The most defensible article, paper, or internal report therefore presents a capability claim, a benchmark rationale, a controlled protocol, learning and outcome curves, held-out performance where relevant, uncertainty, and an explicit boundary around the conclusion. No single score captures long-horizon competence in every setting.
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