ai-agent-book/slides/lesson-30.md

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theme	title	info	author	transition	mdc	lineNumbers	monaco	aspectRatio	canvasWidth	layout	class
seriph	Lesson 30 — How Do You Reward a Long Agent Trajectory?	English video course for AI Agents in Depth	Bojie Li	slide-left	true	false	false	16/9	980	cover	cover

Improve · Chapter 7 · Model Post-Training

How Do You Reward a Long Agent Trajectory?

Credit assignment, reward density, process signals, and path penalties

Lesson 30 of 42 · 17 minutes · From Single-Turn to Multi-Turn; Credit Assignment; Process vs. Outcome Reward; RLVP

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The central question

When the final result fails, which earlier tool choice should the model change?

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Why this problem matters

Sparse outcome

One final bit leaves most actions unexplained.

Process evidence

Tool errors and rule violations identify local mistakes.

Partial credit

Reachable progress can rescue information from all-fail groups.

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Three ideas to keep in view

Outcome reward

Score the completed task—not a convenient proxy

Process reward

Evaluate intermediate reasoning or actions

RLVP

Reward the outcome; penalize verified path violations

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The book's visual model

Credit assignment across a multi-turn interaction

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Outcome only vs. Outcome + path evidence

Outcome only

• Objective final target
• Simple verifier
• Very sparse credit

Outcome + path evidence

• Retains final goal
• Uses observed violations
• Denser diagnosis

Path signals should constrain the route without replacing the destination.

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Separate success from path violations

outcome = task_verifier(final_state)
violations = rule_verifier(trajectory)
progress = reachable_subgoals(trajectory)
reward = outcome - penalty(violations)
reward += partial_credit(progress, only_if_all_fail=True)

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Test the claim

7-14 gates2 min

Run verifier regressions for trajectory data

Observe: Malformed samples rejected before they can become supervision

Demo budget: 2 minutes · one contiguous terminal block

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Live demo

Switching to the terminal

$ python -m pytest chapter7/cot-distillation/test_student_pipeline.py chapter7/cot-distillation/test_empty_problems.py -q

Run the command(s), narrate decisions, and point to the observation—not just the output.

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What the evidence supports

Finding 1

Multi-turn tasks turn one outcome into a temporal attribution problem.

Finding 2

Environment feedback contains information that scalar outcome rewards discard.

Finding 3

A process metric becomes dangerous when it is easier to optimize than the real goal.

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Where the claim stops

Boundary condition

An LLM process judge can reward plausible-looking steps that did not causally produce the outcome.

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Engineering takeaway

Design rule

Keep an objective outcome gate, add only externally verified path signals, and test explicitly for reward hacking.

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Continue the experiment

Experiment 7-14: RLVP reproduction guide chapter7/RLVP/ Experiment 7-12: spatial reasoning chapter7/SpatialReasoning/ Experiment 7-13: SimpleVLA-RL chapter7/SimpleVLA-RL/ Reward density spectrum book-en/images/fig7-reward-density.svg

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Pause and apply

Your turn

Which intermediate signal in your Agent is evidence of progress, and which is merely correlated with it?

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Next · Lesson 31

Apply these signals to the combinatorial problem of learning when and how to call tools.

→