AI Engineer · Published 2026-09-10

Mousepower: agents that can’t be measured, can’t be managed. — Maximillian Piras, Yutori

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agentsmeasurementroitokensverificationentropyagent-evaluationverification-bottlenecktask-uncertaintytoken-economicsmodel-routingroi-metrics
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Summary

Overview

  • Speaker: Maximillian Piras
  • Channel: AI Engineer
  • Main topic: Measuring and managing AI agents through mental models and ROI metrics
  • Purpose: To provide AI designers and engineers with a framework for measuring AI agent performance and ROI by aligning metrics with human mental models. Maximillian Piras, founding designer at Yutori, explores the measurement problem in AI agents. Comparing the current wave of agent adoption to James Watt introducing steam engines to replace horse gins, he discusses how agents suffer from a lack of reliable ROI metrics and how execution speed outpaces verification capabilities (Amdahl's law). He introduces a matrix mapping task steps uncertainty and acceptance criteria uncertainty to help build and evaluate AI agents effectively.

Topic Map

The Agent Measurement Problem

  • Explanation: Introduction of the core thesis that agents have a measurement problem: customers feel excited about agents but feel they are scratching the surface because there is no intuitive way to evaluate them or calculate ROI.
  • Key claims:
    • Agents have a measurement problem.
    • Customers feel they are just scratching the surface of agent capabilities.
    • Token costs make it difficult to evaluate ROI.
  • Examples:
    • Founding designer work at Yutori building computer use models.
  • Terminology:
    • agents
    • ROI
    • token cost
    • mental models
  • Why it matters: Without proper measurement, communicating value to customers and justifying agent spend is nearly impossible.

Historical Parallel: James Watt and the Steam Engine

  • Explanation: Drawing parallels between the adoption of steam engines over horse gins in the 1700s and AI agent adoption today, highlighting cognitive dissonance and the invention of 'horsepower' as a communication metric.
  • Key claims:
    • James Watt needed to replace horse gins with steam engines.
    • Horse gins had great vibes and were easy to understand.
    • Watt invented 'horsepower' to communicate the multiplier of efficiency despite not being strictly scientific.
  • Examples:
    • Replacing horse gins in breweries with steam engines.
  • Terminology:
    • horse gin
    • steam engine
    • horsepower
    • cognitive dissonance
  • Why it matters: If you can't measure ROI, you can't communicate value. New technology requires a metric that matches user mental models.

Code Review Bottleneck and Amdahl's Law

  • Explanation: Examining how coding agents increase code generation speed by 8x, but human code review and verification become the new bottleneck.
  • Key claims:
    • Engineers are merging 8x as much code per day as in 2024.
    • Human code review has become a new bottleneck.
    • As execution gets cheap, the bottleneck shifts to verification.
  • Examples:
    • Anthropic team data showing increased code generation with un-solved code review bottlenecks.
  • Terminology:
    • pull requests
    • code review
    • Amdahl's law
    • verification
  • Why it matters: Execution at the speed of compute must be matched by measurement at the speed of compute.

Tokens vs. Outcomes and the Doom Loop

  • Explanation: Discussing the trap of token-maxxing, austerity loops, and the necessity of tying token expenditure directly to clean business outcomes.
  • Key claims:
    • Tokens are an output, not an outcome.
    • The industry is stuck in a doom loop of overspending and underusing (Tokenmaxxing -> Get Bill -> Austerity -> FOMO).
    • Coinbase CEO's example of changing default models to save frontier models for hard tasks.
  • Examples:
    • Coinbase internal default model adjustments.
    • Ramp blog post on token spending.
  • Terminology:
    • tokenmaxxing
    • token usage
    • outcomes
    • austerity loop
  • Why it matters: Focusing solely on tokens leads to misaligned incentives and unsustainable spending.

Information Theory and Entropy in Agent Tasks

  • Explanation: Applying Claude Shannon's information theory and entropy to task steps and acceptance criteria uncertainty.
  • Key claims:
    • Entropy measures uncertainty in a probability distribution.
    • Cross-entropy helps determine agent capability.
    • Tasks vary based on uncertainty in task steps and uncertainty in acceptance criteria.
  • Examples:
    • Booking a flight vs. painting a masterpiece.
  • Terminology:
    • entropy
    • information theory
    • cross-entropy
    • uncertainty
  • Why it matters: Mapping tasks on a 2x2 matrix of uncertainty helps engineers decide where agents are viable versus where verification is indistinguishable from execution.

Key Points

Agents have a measurement problem

  • Explanation: Users and companies struggle to measure the ROI of AI agents, making adoption and scaling difficult despite high enthusiasm.
  • Evidence: Customer discussions at Yutori revealing that users feel they are just scratching the surface.
  • Practical implication: Engineers must develop better evaluation frameworks and metrics for agent output.

Verification bottleneck in coding agents

  • Explanation: While AI generates code much faster, human code review cannot keep up, shifting the bottleneck to verification.
  • Evidence: Engineers merging 8x more code per day while code review remains manual and bottlenecked.
  • Practical implication: We need agents that assist with verification and code review, not just code generation.

Tokens are outputs, not outcomes

  • Explanation: Measuring success by token volume is misleading; success should be measured by business outcomes and bug-free completions.
  • Evidence: The contrast between tracking token spend versus tracking closed support requests or bugs fixed.
  • Practical implication: Align agent metrics with business value rather than raw compute consumption.

Frameworks, Models & Processes

Task Uncertainty Matrix

  • How it works: A 2x2 matrix mapping uncertainty in task steps against uncertainty in acceptance criteria to evaluate agent suitability.
  • Components:
    • Uncertainty in Task Steps (Low/High)
    • Uncertainty in Acceptance Criteria (Low/High)
    • Write a script, save tokens (Low steps, low criteria)
    • Data risk of OOD / sparse rewards for RL (High steps, high criteria)
    • Verification indistinguishable from execution / MP-style adversarial agents (Medium uncertainty)
  • When to use: When deciding which tasks to delegate to AI agents and how to verify their outputs.

Examples & Case Studies

James Watt introduced steam engines to replace horse gins in the 1700s.

  • Illustrates: Overcoming the cognitive dissonance of adopting new technology by creating an intuitive metric (horsepower).
  • Lesson: New technology requires metrics that match user mental models to bridge adoption gaps.

Coinbase adjusted internal defaults to restrict frontier models to hard tasks.

  • Illustrates: Preventing token waste and breaking the overspending loop.
  • Lesson: Optimizing model routing saves costs and aligns token usage with task difficulty.

Actionable Takeaways

  • Immediate:
    • Stop measuring agent success purely by token volume.
    • Evaluate tasks based on uncertainty in steps and acceptance criteria before deploying agents.
    • Address verification bottlenecks alongside execution speed.
  • Strategic:
    • Develop intuitive metrics (like horsepower for steam engines) to communicate agent ROI to customers.
    • Align agent incentives with business outcomes rather than raw output generation.
  • Questions to investigate:
    • How do we measure verification quality at the speed of compute?
    • What is the modern equivalent of 'horsepower' for AI agents?

Claims Worth Verifying

  • The typical engineer was merging 8x as much code per day in Q2 2026 as in 2024. (statistical)

Notable Quotes

"Agents have a measurement problem." (at 6:51) "It is difficult to get a man to understand something, when his salary depends upon his not understanding it." (at 7:10) "If we can't measure ROI, we can't communicate value." (at 14:10) "Tokens are an output. We want outcomes instead." (at 17:28) "In the second quarter of 2026, the typical engineer was merging 8x as much code per day as they were in 2024..." (at 19:16) "The pull request is not dying because someone invented a better review surface. It is dying because the assumptions underneath it no longer hold." (at 20:49)

Compressed Summary

  • AI agents face a severe measurement and ROI problem.
  • Historical parallel: James Watt invented 'horsepower' to sell steam engines by matching horse-based mental models.
  • Amdahl's law applies to AI engineering: as execution gets cheap, verification becomes the bottleneck.
  • Tokens are outputs; agents must be measured by business outcomes.
  • A task uncertainty matrix helps determine whether an agent is suitable for a given workflow.
  • Keywords: agents, measurement, roi, tokens, verification, entropy
  • Core insight: To successfully adopt AI agents, we must move beyond measuring token consumption and establish ROI metrics and verification frameworks that fit user mental models.

Core insights

6
Mental Modelhigh noveltymoderate evidence

The viability boundary for agent automation is defined by whether verification is distinguishable from execution: when acceptance criteria are highly uncertain, checking the output costs as much as producing it, so delegation yields no leverage regardless of how fast the agent executes.

Why it matters

It gives engineers a principled stopping rule for agent delegation that does not depend on model quality — some tasks cannot be automated away because the verifier, not the generator, is the load-bearing component.

Generalization

Any pipeline where the cost of checking correctness approaches the cost of producing the artifact (high-criteria-uncertainty work like design, research, or open-ended judgment) will not benefit from faster generation; effort should shift to making verification cheaper or making criteria crisper.

Verification indistinguishable from execution / MP-style adversarial agents (Medium uncertainty)
Open source video
Mapping tasks on a 2x2 matrix of uncertainty helps engineers decide where agents are viable versus where verification is indistinguishable from execution.
Open source video
Mechanismmedium noveltymoderate evidence

Amdahl's law applied to agent pipelines: as execution cost collapses, the non-parallelizable human verification stage dominates end-to-end throughput — evidenced by engineers merging 8x more code while review stays manual.

Why it matters

Optimizing generation throughput past a certain point produces zero end-to-end gain; the correct engineering target becomes the verifier, not the generator. This argues for verification-assist agents and review-budget accounting as first-class system components.

Generalization

In any agentic pipeline, identify the serialized human stage and measure its cost share before optimizing the automated stages; speedups in already-cheap stages are wasted.

Engineers are merging 8x as much code per day as in 2024.
Open source video
Human code review has become a new bottleneck.
Open source video
As execution gets cheap, the bottleneck shifts to verification.
Open source video
Practicehigh noveltymoderate evidence

Task triage by two-axis uncertainty (uncertainty in task steps vs uncertainty in acceptance criteria) is a routing primitive: each quadrant implies a different build/verification strategy, and the high/high quadrant maps directly onto RL problems with sparse rewards and OOD data risk.

Why it matters

It converts 'should we use an agent here?' from intuition into a decomposition question, and it links product triage to training concerns (reward sparsity, distribution shift) in the same framework.

Generalization

Before choosing an agent architecture, decompose the task into step-uncertainty and criteria-uncertainty; the quadrant determines whether you need better planning, better verifiers, or a different training signal.

A 2x2 matrix mapping uncertainty in task steps against uncertainty in acceptance criteria to evaluate agent suitability.
Open source video
Data risk of OOD / sparse rewards for RL (High steps, high criteria)
Open source video
Write a script, save tokens (Low steps, low criteria)
Open source video
Failure Modemedium noveltymoderate evidence

Token volume is an output metric masquerading as an outcome metric; the industry oscillates through a Tokenmaxxing -> Get Bill -> Austerity -> FOMO cycle because spend is not tied to a closed business outcome.

Why it matters

Token telemetry is cheap to instrument and therefore becomes the de facto KPI, producing misaligned incentives and boom/bust budgeting. Instrumentation should bind agent runs to outcome events (closed tickets, bugs fixed) rather than consumption.

Generalization

Any agent platform that reports consumption metrics without outcome attribution will drift into the same cycle; observability design must include outcome join keys at run level.

Tokens are an output, not an outcome.
Open source video
The industry is stuck in a doom loop of overspending and underusing (Tokenmaxxing -> Get Bill -> Austerity -> FOMO).
Open source video
Practicemedium noveltymoderate evidence

Difficulty-based model routing is the concrete mitigation for token waste: reserve frontier models for hard tasks and let defaults handle the rest.

Why it matters

It turns cost control from a budgeting exercise into a runtime routing policy, which must live in the agent harness rather than in user behavior.

Generalization

A routing layer that classifies task difficulty before model selection is a reusable cost/quality knob, and it interacts directly with the uncertainty matrix as the classifier.

Coinbase CEO's example of changing default models to save frontier models for hard tasks.
Open source video
Optimizing model routing saves costs and aligns token usage with task difficulty.
Open source video
Mental Modelmedium noveltyweak evidence

Adoption of a new execution paradigm requires a metric expressed in the incumbent's mental model (horsepower vs horse gins), even if that metric is not strictly scientific — the measurement problem is a communication problem as much as a technical one.

Why it matters

Framing agent value in units the buyer already reasons about (labor-equivalents, cost per completed task) may unlock adoption when raw capability benchmarks do not.

Generalization

When introducing an automation capability, the binding constraint is often a comparable unit of value against the replaced process, not the capability itself.

Watt invented 'horsepower' to communicate the multiplier of efficiency despite not being strictly scientific.
Open source video
New technology requires a metric that matches user mental models.
Open source video

Deep dives

5

Estimating acceptance-criteria uncertainty before delegation

Research question

Can acceptance-criteria uncertainty be estimated before or during task execution with proxies that predict whether verification will cost as much as execution?

Why

The viability boundary for agent delegation hinges on criteria uncertainty, but the summary offers the axis without an estimator. Without measurable proxies, the 2x2 matrix remains a reasoning device rather than a routing policy.

He introduces a matrix mapping task steps uncertainty and acceptance criteria uncertainty to help build and evaluate AI agents effectively.
Open source video
As execution gets cheap, the bottleneck shifts to verification.
Open source video
Source video

Measuring the serialized verification fraction in agent pipelines

Research question

What is the achievable end-to-end speedup ceiling for agent pipelines given a fixed human verification stage, and how much does verification-assist tooling move it?

Why

Amdahl's law is invoked qualitatively. Measuring the serial fraction turns the bottleneck from an anecdote into an optimization target and tells teams when further generation speedup is wasted.

Engineers are merging 8x as much code per day as in 2024.
Open source video
Human code review has become a new bottleneck.
Open source video
As execution gets cheap, the bottleneck shifts to verification.
Open source video
Source video

Outcome-attributed telemetry for agent runs

Research question

Does joining token spend to outcome events at the run level break the overspend-then-austerity cycle in agent budgets?

Why

Token telemetry is cheap to instrument and becomes the de facto KPI, producing misaligned incentives. Outcome attribution changes the observable unit from consumption to business result.

Tokens are an output, not an outcome.
Open source video
The industry is stuck in a doom loop of overspending and underusing (Tokenmaxxing -> Get Bill -> Austerity -> FOMO).
Open source video
Source video

Runtime difficulty routing as a classifier over uncertainty axes

Research question

Can the step-uncertainty and criteria-uncertainty axes serve as a classifier input for routing tasks to model tiers without degrading task success?

Why

Difficulty-based routing is a concrete cost mitigation, but it needs a classifier. The uncertainty matrix is the natural candidate, linking triage directly to model selection.

Token costs make it difficult to evaluate ROI.
Open source video
The industry is stuck in a doom loop of overspending and underusing (Tokenmaxxing -> Get Bill -> Austerity -> FOMO).
Open source video
Source video

Designing an agent-era unit of value that buyers already believe

Research question

What unit of agent value maps onto an existing business mental model and remains honest enough to survive procurement scrutiny?

Why

The horsepower analogy suggests adoption depends on a comparable unit against the replaced process, not on raw capability benchmarks. The summary identifies the need but proposes no unit.

Watt invented 'horsepower' to communicate the multiplier of efficiency despite not being strictly scientific.
Open source video
New technology requires a metric that matches user mental models.
Open source video
Source video

Article ideas

4

The Verification Ceiling: Why Faster Agents Don't Ship Faster Software

Once generation is cheap, end-to-end throughput is capped by the serialized human verification stage, so engineering effort should shift from generator speed to verifier throughput and review-assist tooling.

Angle

Use Amdahl's law and the 8x merge-volume observation to argue that agent platforms must budget and instrument the verifier as a first-class pipeline component.

Source video

Tokens Are Not a Business Metric: Breaking the Agent Spend Doom Loop

Reporting token consumption as the primary agent KPI creates the Tokenmaxxing -> Get Bill -> Austerity -> FOMO cycle because spend is not tied to a closed outcome; observability must join runs to outcome events.

Angle

Trace how output metrics become organizational incentives, then propose run-level outcome attribution as the instrumentation schema change that breaks the cycle.

Source video

A 2x2 Matrix Is Not Enough: Operationalizing Uncertainty in Agent Delegation

The step-uncertainty x criteria-uncertainty matrix is a useful triage primitive, but it becomes an engineering tool only when each axis has measurable proxies that predict verification cost and agent leverage.

Angle

Take the matrix seriously as a routing system and specify the missing estimators, starting with criteria-uncertainty proxies such as verifier disagreement and historical rework.

Source video

What Is Agent Horsepower? Designing a Unit of Value Buyers Already Believe

Agent adoption is constrained less by capability than by the absence of a metric expressed in the buyer's existing mental model, so the field needs a comparable unit of value rather than another benchmark.

Angle

Use the James Watt horsepower story as precedent and evaluate candidate units such as verified tasks completed, review-hours displaced, and cost per accepted artifact.

Source video

Project ideas

4

Verification Fraction Profiler

beyond-evals

Instrumenting the human review stage will show that it is the dominant serial fraction in agent-assisted code pipelines, and deploying review-assist agents will reduce that fraction enough to improve end-to-end cycle time.

Proof of concept

Build a CI-integrated profiler that measures time and cost split between generation, automated checks, and human review per pull request, then run a before/after trial with a review-assist agent on a subset of repositories.

Measurement

Serial verification fraction, review latency per PR, merge volume, end-to-end cycle time, and review defect escape rate before and after review-assist deployment.

Source video

Criteria Uncertainty Estimator

gatehouse

A pre-execution proxy score for acceptance-criteria uncertainty will predict verification cost and end-to-end agent leverage better than step-uncertainty or single-axis task difficulty.

Proof of concept

Train a lightweight classifier on task descriptions, historical rework rates, verifier disagreement, and rubric coverage, then route tasks to agent delegation, human ownership, or tightened-criteria treatment.

Measurement

Correlation between proxy score and measured verification cost, delegation leverage, rework rate, and misrouting rate across held-out task batches.

Source video

Outcome-Attributed Token Ledger

new

Joining agent run telemetry to outcome events such as closed tickets or fixed bugs will produce ROI-per-task reporting that reduces budget oscillation compared with token-spend dashboards.

Proof of concept

Instrument an agent platform so every run carries an outcome join key, then expose per-outcome cost, spent tokens, accepted artifacts, and outcome-per-dollar in a shared dashboard.

Measurement

Variance in monthly agent spend, cost per accepted artifact, outcome attribution coverage, and correlation between spend changes and outcome changes over two budget cycles.

Source video

Uncertainty-Axis Model Router

movement-lab

Routing tasks to model tiers using step-uncertainty and criteria-uncertainty classifications will maintain task-success rates while reducing cost per accepted artifact compared with a frontier-model default.

Proof of concept

Implement a routing layer that classifies incoming tasks on the two uncertainty axes and escalates only high-uncertainty cases to frontier models, with a control group on frontier defaults.

Measurement

Cost per accepted task, task-success rate, escalation rate, verifier disagreement, and quality regression across routed versus default configurations.

Source video

Architectural implications

4

Verification becomes the throughput ceiling once generation is cheap.

Before

Agent pipelines optimize the generation loop — prompt quality, tool throughput, parallelism of producers.

After

Pipelines budget and instrument the verification stage, and may deploy dedicated agents that assist review and checking.

Consequence

Verification-assist agents, review queues with throughput SLAs, and verifier cost accounting become first-class components; generator speed is downgraded as an optimization target.

Source video

Model selection becomes a per-task runtime decision rather than a global default.

Before

A single frontier model is configured as the default for all agent calls.

After

A routing layer classifies incoming task difficulty and escalates to frontier models only for hard work.

Consequence

The harness gains a classifier component coupled to the uncertainty axes, and cost/quality behavior becomes tunable without changing prompts.

Source video

Task triage precedes delegation.

Before

All requests flow into the same agent loop with the same verification procedure.

After

Requests are placed on the step-uncertainty x criteria-uncertainty matrix, and the quadrant selects the verification strategy (or rejects delegation).

Consequence

Triage becomes an explicit boundary in the system with its own accuracy requirements; misclassification produces either wasted verification or unverified output.

Source video

Telemetry must join consumption to outcome.

Before

Observability reports token spend, latency, and call counts per agent run.

After

Runs carry outcome identifiers (ticket closed, bug fixed, task accepted) that spend can be attributed against.

Consequence

Instrumentation schema changes at the run level, enabling ROI-per-task reporting instead of per-token reporting.

Source video

Tradeoffs and failure modes

4

Frontier-model-by-default vs routed defaults

Benefit

Frontier defaults maximize per-task capability and remove the need for a difficulty classifier.

Cost or risk

Unsustainable token burn that triggers the austerity cycle, and misaligned incentives where consumption becomes the visible metric.

The industry is stuck in a doom loop of overspending and underusing (Tokenmaxxing -> Get Bill -> Austerity -> FOMO).
Open source video
Source video

Faster generation without matched verification

Benefit

Coding agents raise merge volume substantially, increasing raw output per engineer.

Cost or risk

Human review becomes the serialized bottleneck, so end-to-end delivery does not improve proportionally and review quality is at risk.

Engineers are merging 8x as much code per day as in 2024.
Open source video
Source video

Verification indistinguishable from execution

Benefit

Agents can be pointed at high-criteria-uncertainty work where generation is feasible.

Cost or risk

No net leverage is gained because checking the output costs the same as producing it; the task must remain human-owned or the criteria must be tightened.

Mapping tasks on a 2x2 matrix of uncertainty helps engineers decide where agents are viable versus where verification is indistinguishable from execution.
Open source video
Source video

Measuring capability with information-theoretic quantities

Benefit

Entropy and cross-entropy offer a task-relative, non-anthropomorphic way to express agent capability.

Cost or risk

The summary provides no mechanism for estimating these quantities per task, so it risks remaining metaphor rather than instrument.

Cross-entropy helps determine agent capability.
Open source video
Source video

Open questions

4

How can uncertainty in acceptance criteria be estimated before or during task execution, given that it is the axis that decides whether verification is separable from execution?

Why unresolved

The summary presents the axis but no estimation procedure; the matrix is stated as a reasoning device, not a measurable input.

Research direction

Define proxies for criteria uncertainty (e.g., verifier disagreement rates, rubric coverage, historical rework rates) and test whether they predict end-to-end agent leverage.

Source video

What is the achievable speedup ceiling for agent pipelines given a fixed human verification stage, and how much does verification-assist tooling move it?

Why unresolved

Amdahl's law is invoked qualitatively; no measured serial fraction or post-verification-assist figure is given.

Research direction

Instrument code-review pipelines to measure the serialized verification fraction before and after deploying review-assist agents.

Source video

Can entropy or cross-entropy be computed as a production capability metric per task distribution, or is it only an offline analytic framing?

Why unresolved

The summary asserts cross-entropy helps determine capability without describing an estimator, data source, or operationalization.

Research direction

Propose a task-conditional cross-entropy estimator over agent trajectories and correlate it against held-out success rates.

Source video

What is the agent-era equivalent of 'horsepower' — a unit that maps agent output onto an existing business mental model and remains honest?

Why unresolved

The analogy is historical; the summary identifies the need for a mental-model-matched metric but does not propose one.

Research direction

Prototype candidate units (verified tasks completed, review-hours displaced, cost per accepted artifact) and test which correlates with purchasing decisions.

Source video

Key claims

7
opinionVerification not requested

Agents have a measurement problem that prevents customers from assessing value.

Evidence

Agents have a measurement problem.

Source video
causalVerification needed

Human code review has become the bottleneck while agent-assisted code generation scaled roughly 8x.

Evidence

Engineers are merging 8x as much code per day as in 2024.

Question

Is the 8x merge figure attributable to agent-generated code, and what proportion of the pipeline time is now spent in review?

Source video
causalVerification needed

As execution becomes cheap, the constraint migrates to verification.

Evidence

As execution gets cheap, the bottleneck shifts to verification.

Question

Does the verification stage dominate end-to-end latency/cost in measured agent deployments across domains other than coding?

Source video
comparativeVerification not requested

Tokens are an output rather than a business outcome.

Evidence

Tokens are an output, not an outcome.

Source video
causalVerification needed

Token spending oscillates in a recurring overspend-then-austerity cycle driven by lacking outcome attribution.

Evidence

The industry is stuck in a doom loop of overspending and underusing (Tokenmaxxing -> Get Bill -> Austerity -> FOMO).

Question

Can the cycle be observed in organizational spend data, and does outcome-linked measurement break it?

Source video
causalVerification needed

Task suitability for agents can be characterized by uncertainty in task steps and uncertainty in acceptance criteria.

Evidence

Tasks vary based on uncertainty in task steps and uncertainty in acceptance criteria.

Question

Do the two uncertainty axes predict agent success and verification cost better than single-axis task difficulty?

Source video
causalVerification needed

High step-uncertainty combined with high criteria-uncertainty yields sparse-reward RL problems and out-of-distribution data risk.

Evidence

Data risk of OOD / sparse rewards for RL (High steps, high criteria)

Question

Is the high/high quadrant empirically associated with reward sparsity and OOD failure in trained agents?

Source video

Connections

4