ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）
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---
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theme: seriph
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title: "Lesson 30 — How Do You Reward a Long Agent Trajectory?"
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info: "English video course for AI Agents in Depth"
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author: Bojie Li
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transition: slide-left
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mdc: true
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lineNumbers: false
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monaco: false
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aspectRatio: 16/9
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canvasWidth: 980
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layout: cover
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class: cover
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---
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<div class="course-kicker">Improve · Chapter 7 · Model Post-Training</div>
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# How Do You Reward a Long Agent Trajectory?
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<p class="course-subtitle">Credit assignment, reward density, process signals, and path penalties</p>
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<div class="course-cover-meta">Lesson 30 of 42 · 17 minutes · From Single-Turn to Multi-Turn; Credit Assignment; Process vs. Outcome Reward; RLVP</div>
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<!-- Presenter cue: Open with the concrete problem. Add personal context in your own words; do not read the slide. -->
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---
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layout: center
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class: text-center
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---
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<div class="course-kicker">The central question</div>
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<div class="course-big">When the final result fails, which earlier tool choice should the model change?</div>
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<!-- Presenter cue: Let the question sit for a moment, then state the failure mode the lesson will explain. -->
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---
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# Why this problem matters
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<div class="grid grid-cols-3 gap-5 mt-6">
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<div class="course-card blue">
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<h3>Sparse outcome</h3>
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<p>One final bit leaves most actions unexplained.</p>
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</div>
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<div class="course-card green">
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<h3>Process evidence</h3>
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<p>Tool errors and rule violations identify local mistakes.</p>
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</div>
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<div class="course-card orange">
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<h3>Partial credit</h3>
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<p>Reachable progress can rescue information from all-fail groups.</p>
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</div>
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</div>
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<!-- Presenter cue: Connect each card to a product or experiment consequence. -->
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---
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# Three ideas to keep in view
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<div class="grid grid-cols-3 gap-5 mt-6">
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<div class="course-card purple">
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<h3>Outcome reward</h3>
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<p>Score the completed task—not a convenient proxy</p>
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</div>
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<div class="course-card blue">
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<h3>Process reward</h3>
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<p>Evaluate intermediate reasoning or actions</p>
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</div>
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<div class="course-card green">
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<h3>RLVP</h3>
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<p>Reward the outcome; penalize verified path violations</p>
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</div>
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</div>
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<!-- Presenter cue: Define unfamiliar terms in plain language; the audience is new to ML training and RL. -->
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---
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# The book's visual model
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<img class="course-figure" src="/images/fig7-15.svg" alt="Credit assignment across a multi-turn interaction">
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<div class="course-caption">Credit assignment across a multi-turn interaction</div>
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<!-- Presenter cue: Trace the diagram in one direction and name the mechanism that matters for this lesson. -->
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---
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# Outcome only vs. Outcome + path evidence
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<div class="grid grid-cols-2 gap-6 mt-5">
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<div class="course-card orange"><h3>Outcome only</h3><ul><li>Objective final target</li><li>Simple verifier</li><li>Very sparse credit</li></ul></div>
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<div class="course-card green"><h3>Outcome + path evidence</h3><ul><li>Retains final goal</li><li>Uses observed violations</li><li>Denser diagnosis</li></ul></div>
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</div>
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<div class="course-caption course-caption-strong">Path signals should constrain the route without replacing the destination.</div>
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<!-- Presenter cue: Explain the trade-off; avoid presenting the right column as universally superior. -->
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---
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# Separate success from path violations
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~~~python
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outcome = task_verifier(final_state)
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violations = rule_verifier(trajectory)
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progress = reachable_subgoals(trajectory)
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reward = outcome - penalty(violations)
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reward += partial_credit(progress, only_if_all_fail=True)
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~~~
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<!-- Presenter cue: Walk through the executable idea line by line; keep implementation details for the terminal. -->
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---
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# Test the claim
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<div class="grid grid-cols-1 gap-4 mt-5">
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<div class="course-card blue">
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<div class="course-demo-head"><span>7-14 gates</span><span>2 min</span></div>
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<h3>Run verifier regressions for trajectory data</h3>
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<p><strong>Observe:</strong> Malformed samples rejected before they can become supervision</p>
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</div>
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</div>
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<div class="course-caption course-caption-strong">Demo budget: 2 minutes · one contiguous terminal block</div>
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<!-- Presenter cue: State the prediction before running anything. Name the observation that could disconfirm it. -->
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---
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class: course-terminal
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---
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<div class="course-kicker">Live demo</div>
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# Switching to the terminal
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~~~bash
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$ python -m pytest chapter7/cot-distillation/test_student_pipeline.py chapter7/cot-distillation/test_empty_problems.py -q
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~~~
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<div class="course-terminal-watch">Run the command(s), narrate decisions, and point to the observation—not just the output.</div>
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<!-- Presenter cue: Switch windows now. Return to the next slide after every listed experiment is complete. -->
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---
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# What the evidence supports
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<div class="grid grid-cols-3 gap-5 mt-6">
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<div class="course-card green">
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<h3>Finding 1</h3>
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<p>Multi-turn tasks turn one outcome into a temporal attribution problem.</p>
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</div>
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<div class="course-card blue">
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<h3>Finding 2</h3>
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<p>Environment feedback contains information that scalar outcome rewards discard.</p>
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</div>
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<div class="course-card purple">
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<h3>Finding 3</h3>
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<p>A process metric becomes dangerous when it is easier to optimize than the real goal.</p>
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</div>
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</div>
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<!-- Presenter cue: Tie each finding to something viewers just observed; distinguish evidence from interpretation. -->
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---
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layout: center
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---
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<div class="course-kicker course-kicker-red">Where the claim stops</div>
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# Boundary condition
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<div class="course-boundary">An LLM process judge can reward plausible-looking steps that did not causally produce the outcome.</div>
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<!-- Presenter cue: Say explicitly what this experiment does not establish. -->
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---
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layout: center
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---
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<div class="course-kicker">Engineering takeaway</div>
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# Design rule
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<div class="course-rule">Keep an objective outcome gate, add only externally verified path signals, and test explicitly for reward hacking.</div>
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<!-- Presenter cue: Present this as a reusable decision rule, then give one counterexample or trade-off. -->
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---
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# Continue the experiment
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<div class="grid grid-cols-2 gap-4 mt-6">
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<a class="course-link" href="../chapter7/RLVP/">
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<span class="course-link-title">Experiment 7-14: RLVP reproduction guide</span>
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<span class="course-link-path">chapter7/RLVP/</span>
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</a>
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<a class="course-link" href="../chapter7/SpatialReasoning/">
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<span class="course-link-title">Experiment 7-12: spatial reasoning</span>
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<span class="course-link-path">chapter7/SpatialReasoning/</span>
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</a>
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<a class="course-link" href="../chapter7/SimpleVLA-RL/">
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<span class="course-link-title">Experiment 7-13: SimpleVLA-RL</span>
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<span class="course-link-path">chapter7/SimpleVLA-RL/</span>
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</a>
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<a class="course-link" href="../book-en/images/fig7-reward-density.svg">
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<span class="course-link-title">Reward density spectrum</span>
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<span class="course-link-path">book-en/images/fig7-reward-density.svg</span>
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</a>
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</div>
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<!-- Presenter cue: Point viewers to the companion paths; do not walk through every extension. -->
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---
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layout: center
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class: text-center
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---
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<div class="course-kicker">Pause and apply</div>
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# Your turn
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<div class="course-big course-reflection">Which intermediate signal in your Agent is evidence of progress, and which is merely correlated with it?</div>
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<!-- Presenter cue: Invite viewers to pause the video. Offer your own answer after a short beat. -->
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---
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layout: center
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class: text-center
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---
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<div class="course-kicker">Next · Lesson 31</div>
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<div class="course-next">Apply these signals to the combinatorial problem of learning when and how to call tools.</div>
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<div class="course-next-arrow">→</div>
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<!-- Presenter cue: Use this transition to make the course feel continuous rather than episodic. -->
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