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\title[JitMem]{Just-in-Time Memory}
\subtitle{Learning to Curate Task-Adaptive Memory for LLM Agents}
\author{Paper authors: Yefan Zhou, Yang Li, Zeyu Leo Liu,\\Semih Yavuz, Shafiq Joty}
\institute{Salesforce AI Research\\[2mm]Independent paper-reading presentation}
\date{arXiv:2609.27334v1\enspace|\enspace23 September 2026\\Reading date: 27 September 2026}

\begin{document}

% 1. 45 seconds. Establish the timing decision as the central contribution.
\begin{frame}
  \titlepage
  \vspace{-5mm}
  \begin{center}\color{AcademicTeal}
  Preserve experience; curate it when the next task is known.
  \end{center}
\end{frame}

% 2. 90 seconds. Use a household trajectory with more than one possible lesson.
\begin{frame}{The next task determines which details matter}
  \begin{columns}[T,onlytextwidth]
  \begin{column}{0.47\textwidth}
    \begin{block}{Write-time curation}
      Finish a task\\$\downarrow$\\
      Commit to a fixed summary\\$\downarrow$\\
      Retrieve it for a future task
    \end{block}
    Useful details may disappear before their value is known.
  \end{column}
  \begin{column}{0.47\textwidth}
    \begin{block}{Read-time curation}
      Store a raw trajectory\\$\downarrow$\\
      Observe the new task\\$\downarrow$\\
      Retrieve and produce tailored guidance
    \end{block}
    One past experience can support different future lessons.
  \end{column}
  \end{columns}
  \papersource{Paper, Sec. 1, PDF pp. 1--2. Diagram is a teaching reconstruction.}
\end{frame}

% 3. 90 seconds. Follow retrieval, curation, execution, and quality-gated storage.
\begin{frame}{A temporary briefing connects memory to action}
  \begin{columns}[c,onlytextwidth]
  \begin{column}{0.69\textwidth}
    \includegraphics[width=\linewidth,height=0.69\textheight,keepaspectratio]{figures/fig-01a-inference.pdf}
  \end{column}
  \begin{column}{0.28\textwidth}
    \begin{itemize}
      \item Retrieve raw traces.
      \item Curate for task $x_t$.
      \item Execute with payload $p_t$.
      \item Judge and store the new trace.
    \end{itemize}
    \medskip
    \alert{The payload is not stored.}
  \end{column}
  \end{columns}
  \papersource{Paper, Fig. 1(a), PDF p. 2; Sec. 3, p. 4. Original figure panel, cropped.}
\end{frame}

% 4. 75 seconds. "Lossless" concerns accepted traces, not the entire task history.
\begin{frame}{Raw storage and selective admission are distinct}
  \[
    \hat{\boldsymbol\xi}_t=\mathcal R(x_t,\mathcal M_t),
    \qquad
    p_t\sim\pi_\phi(\cdot\mid x_t,\hat{\boldsymbol\xi}_t)
  \]
  \begin{itemize}
    \item \textbf{Bank $\mathcal M_t$:} complete task--observation--action traces.
    \item \textbf{Retriever $\mathcal R$:} BM25 over task descriptions; top $k=3$.
    \item \textbf{Curator $\pi_\phi$:} relevant experience, reusable strategy, current-task guidance.
    \item \textbf{Admission:} the executor model judges whether a completed trace succeeded.
  \end{itemize}
  \begin{block}{Scope of preservation}
    Accepted traces remain unabstracted. Rejected traces are discarded.
  \end{block}
  \papersource{Paper, Sec. 3, PDF p. 4; Appendix A, p. 18.}
\end{frame}

% 5. 2 minutes. Explain group-relative advantage and the task-level credit boundary.
\begin{frame}{The current task directly evaluates its own briefing}
  For one task, sample $G=8$ payloads; run the frozen executor on each.
  \[
    \hat A_i=r_t^{(i)}-\frac{1}{G}\sum_{j=1}^G r_t^{(j)}
  \]
  \[
    \mathcal L_{\mathrm{GRPO}}=
    -\frac{1}{G}\sum_{i=1}^G\hat A_i\,
    \log\pi_\phi\!\left(p_t^{(i)}\mid x_t,\hat{\boldsymbol\xi}_t\right)
  \]
  \begin{itemize}
    \item $r_t^{(i)}$: environment reward; $\hat A_i$: centered group advantage.
    \item No advantage standard-deviation normalization; no value network.
    \item Credit is immediate \emph{across tasks}; execution still takes multiple actions.
  \end{itemize}
  \papersource{Paper, Sec. 3, PDF pp. 4--5; equations are unnumbered. Table 7 additionally specifies clipping and KL loss.}
\end{frame}

% 6. 75 seconds. The diagram's reward is ground truth, unlike deployment admission.
\begin{frame}{Only the curator learns from task rewards}
  \begin{columns}[c,onlytextwidth]
  \begin{column}{0.41\textwidth}
    \includegraphics[width=\linewidth,height=0.68\textheight,keepaspectratio]{figures/fig-01b-training.pdf}
  \end{column}
  \begin{column}{0.55\textwidth}
    \begin{itemize}
      \item Start from a Qwen3-8B curator, with thinking disabled.
      \item Train with a frozen Qwen3-8B executor and a \textbf{fixed} training bank.
      \item Use binary success on ALFWorld; continuous score on WebShop.
      \item 100 updates; batch 32; learning rate $10^{-6}$.
    \end{itemize}
    \medskip
    \alert{No curator RL experiments on $\tau^2$-bench.}
  \end{column}
  \end{columns}
  \papersource{Paper, Fig. 1(b), p. 2; Sec. 4.1, p. 7; Table 7, p. 19.}
\end{frame}

% 7. 90 seconds. Prevent confusing online memory updates with online RL.
\begin{frame}{Training uses a fixed bank; testing starts empty}
  \centering
  \begin{tabular}{lll}
  \toprule
  & \textbf{Curator training} & \textbf{Default evaluation}\\
  \midrule
  Initial bank & Training-set base traces & Empty bank\\
  Admission & Ground-truth success & LLM judge\\
  Bank updates & Fixed bank & After each test batch\\
  Weights & Curator learns & Frozen\\
  True reward & Train the curator & Score results only\\
  \bottomrule
  \end{tabular}
  \medskip
  \begin{block}{A batched streaming protocol}
    Tasks within a batch share the same bank state. Task order and batch size can affect performance.
  \end{block}
  \papersource{Paper, Sec. 3, PDF p. 5. The training bank does not carry into default testing.}
\end{frame}

% 8. 75 seconds. Explain score versus exact success, and standard deviation.
\begin{frame}{Three benchmarks test different kinds of reuse}
  \centering
  \begin{tabular}{p{2.35cm}p{4.25cm}p{2.4cm}p{3.45cm}}
  \toprule
  \textbf{Benchmark} & \textbf{Task} & \textbf{Metric $\uparrow$} & \textbf{Protocol}\\
  \midrule
  ALFWorld & Text-based embodied control & SR & 140 tasks; 3 runs\\
  WebShop & Product purchase with constraints & Score, SR & 500 tasks; 3 runs\\
  $\tau^2$-bench & Airline, retail, telecom dialogues & Domain and average SR & 4 runs; prompted curators only\\
  \bottomrule
  \end{tabular}
  \medskip
  \begin{itemize}
    \item Batch sizes: 10 / 10 / 5; main retrieval count: $k=3$.
    \item Reported uncertainty: standard deviation across task orderings.
    \item WebShop score includes partial credit; SR measures successful tasks.
  \end{itemize}
  \papersource{Paper, Sec. 4, p. 5; Tables 1--2, pp. 6--7; Appendix A, p. 18.}
\end{frame}

% 9. 60 seconds. Curator and executor are separate model choices.
\begin{frame}{Keep curator capacity separate from executor capacity}
  \begin{columns}[T,onlytextwidth]
  \begin{column}{0.48\textwidth}
    \begin{block}{Comparison methods}
      \begin{itemize}
        \item No Memory: executor alone.
        \item ReasoningBank: written strategies.
        \item MemP: multilevel procedural items.
        \item SkillOS: learned skill curation at write time.
      \end{itemize}
    \end{block}
  \end{column}
  \begin{column}{0.48\textwidth}
    \begin{block}{Variant names}
      \begin{itemize}
        \item \texttt{-base}: prompted Qwen3-8B.
        \item \texttt{-gemini/-gpt}: prompted stronger curator.
        \item \jit: RL-trained Qwen3-8B curator.
      \end{itemize}
    \end{block}
    Executors: Qwen3-8B, Gemini-2.5-Pro, or GPT-5.4.
  \end{column}
  \end{columns}
  \papersource{Paper, Secs. 2 and 4, PDF pp. 3, 5. Descriptions of prior work follow this paper.}
\end{frame}

% 10. 90 seconds. Headline gains refer to Qwen/Qwen and absolute percentage points.
\begin{frame}{With Qwen, learned read-time curation wins clearly}
  \textbf{Qwen3-8B executor and curator base model; 3 runs.}
  \medskip
  \centering
  \begin{tabular}{lrrr}
  \toprule
  \textbf{Method} & \textbf{ALFWorld SR} & \textbf{WebShop score} & \textbf{WebShop SR}\\
  \midrule
  No Memory & \sr{47.9}{1.2} & \sr{33.3}{0.7} & \sr{9.8}{0.5}\\
  SkillOS (RL) & \sr{61.2}{4.6} & \sr{40.6}{0.7} & \sr{16.5}{0.7}\\
  \jit-base & \sr{60.5}{2.6} & \sr{32.5}{3.2} & \sr{11.7}{0.5}\\
  \jit\ (RL) & \textbf{77.4}$\pm$2.9 & \textbf{61.1}$\pm$0.9 & \textbf{32.8}$\pm$1.7\\
  \bottomrule
  \end{tabular}
  \begin{block}{Against the trained write-time baseline}
    ALFWorld: \alert{+16.2 percentage points}.\qquad
    WebShop SR: \alert{+16.3 points}.
  \end{block}
  \papersource{Selected rows from paper Table 1, PDF p. 6. Mean $\pm$ SD; metrics shown on a 0--100 scale. Baseline provenance: backup slide 23.}
\end{frame}

% 11. 75 seconds. The untrained evidence is important but not universal.
\begin{frame}{An untrained curator helps in some configurations}
  \textbf{WebShop SR, matched curator and executor models.}
  \medskip
  \centering
  \begin{tabular}{lrrr}
  \toprule
  \textbf{Models} & \textbf{ReasoningBank} & \textbf{SkillOS, prompted} & \textbf{JitMem, prompted}\\
  \midrule
  Qwen3-8B & 11.4 & 13.6 & 11.7\\
  Gemini-2.5-Pro & 40.2 & 41.0 & \textbf{61.0}\\
  GPT-5.4 & 33.6 & 26.9 & \textbf{47.5}\\
  \bottomrule
  \end{tabular}
  \medskip
  \begin{itemize}
    \item Read-time design yields large gains with stronger prompted curators.
    \item Qwen is a counterexample to universal training-free superiority.
    \item With Gemini as executor, prompted Gemini beats the RL Qwen curator: 61.0 vs. 50.5.
  \end{itemize}
  \papersource{Paper, Table 1, PDF p. 6. Selected means; see the original table for all standard deviations.}
\end{frame}

% 12. 75 seconds. This is executor transfer, not evidence of cross-domain transfer.
\begin{frame}{A trained curator transfers across executors}
  \textbf{ALFWorld SR; curator base model is Qwen3-8B.}
  \medskip
  \centering
  \begin{tabular}{lrr}
  \toprule
  \textbf{Curator training} & \textbf{Qwen test executor} & \textbf{GPT-5.4 test executor}\\
  \midrule
  No RL & \sr{60.5}{2.6} & \sr{79.3}{3.6}\\
  Train with Qwen & \sr{77.4}{2.9} & \sr{86.7}{0.7}\\
  Train with GPT-5.4 & --- & \sr{88.1}{0.9}\\
  \bottomrule
  \end{tabular}
  \begin{block}{Evidence for modular reuse}
    Transferring the curator reaches within \alert{1.4 SR points} of training it with the target executor.
  \end{block}
  This experiment keeps the benchmark fixed; it does not establish cross-domain transfer.
  \papersource{Paper, Table 3, PDF p. 7; mean $\pm$ SD.}
\end{frame}

% 13. 90 seconds. The +3.9 headline is micro average from prompted GPT, not RL.
\begin{frame}{On $\tau^2$-bench, the largest gain is in telecom}
  \textbf{GPT-5.4 executor; GPT-5.4 prompted curators; no RL.}
  \medskip
  \centering
  \begin{tabular}{lrrrrr}
  \toprule
  \textbf{Method} & \textbf{Airline} & \textbf{Retail} & \textbf{Telecom} & \textbf{Macro} & \textbf{Micro}\\
  \midrule
  No Memory & 65.0 & 79.8 & 50.2 & 65.0 & 65.0\\
  ReasoningBank & 65.0 & 84.6 & 61.6 & 70.4 & 71.7\\
  SkillOS-gpt & 68.0 & 78.5 & 53.3 & 66.6 & 66.4\\
  \jit-gpt & 63.5 & 84.0 & \textbf{72.6} & \textbf{73.4} & \textbf{75.6}\\
  \bottomrule
  \end{tabular}
  \medskip
  \begin{itemize}
    \item Against ReasoningBank: Telecom +11.0; micro average +3.9 points.
    \item No comparable advantage on Airline or Retail.
    \item Micro: \jit\ 75.6$\pm$2.3 vs. ReasoningBank 71.7$\pm$3.9.
  \end{itemize}
  \papersource{Paper, Table 2, PDF p. 7; means over 4 task orderings. Macro weights domains equally; micro aggregates tasks.}
\end{frame}

% 14. 90 seconds. Do not convert executor-side savings into total cost claims.
\begin{frame}{Executor efficiency improves; total cost is unmeasured}
  \textbf{ALFWorld with GPT-5.4 executor, average per task.}
  \medskip
  \centering
  \begin{tabular}{lrrr}
  \toprule
  \textbf{Method} & \textbf{Input tokens (K)} & \textbf{Output tokens (K)} & \textbf{Steps}\\
  \midrule
  No Memory & 9.0 & 1.40 & 17.8\\
  ReasoningBank & 19.7 & 1.26 & 16.2\\
  SkillOS-base & 22.4 & 1.36 & 16.9\\
  \jit-base & 10.9 & 1.00 & 13.2\\
  \jit & \textbf{9.8} & \textbf{0.87} & \textbf{11.6}\\
  \bottomrule
  \end{tabular}\par\medskip
  Input tokens fall 50.3--56.3\% relative to the two write-time methods.
  \par\medskip
  \alert{Executor-side totals exclude curator, judge, retrieval, storage, and training costs.}
  \papersource{Paper, Table 4 and accompanying text, PDF p. 7. Lower is better in all three columns.}
\end{frame}

% 15. 90 seconds. Interventions show contributions, not additive independent effects.
\begin{frame}{Task conditioning, filtering, and raw details all help}
  \textbf{Untrained Qwen curator and Qwen executor; SR.}
  \medskip
  \centering
  \begin{tabular}{lrr}
  \toprule
  \textbf{Variant} & \textbf{ALFWorld} & \textbf{WebShop}\\
  \midrule
  Full \jit-base & \sr{60.5}{2.6} & \sr{11.7}{0.5}\\
  Remove current task from curator & \sr{57.4}{2.0} & \sr{7.1}{0.1}\\
  Store all traces with outcome labels & \sr{59.0}{2.2} & \sr{8.9}{1.7}\\
  Store write-time distilled items & \sr{58.8}{1.5} & \sr{4.9}{0.7}\\
  \bottomrule
  \end{tabular}
  \medskip
  \begin{itemize}
    \item The largest WebShop drop comes from discarding raw details.
    \item Removing the task from the curator still leaves task-based retrieval.
    \item One-factor ablations do not establish additive or independent effects.
  \end{itemize}
  \papersource{Paper, Fig. 2(a), p. 8; exact values from Table 9, p. 21. Mean $\pm$ SD over 3 runs.}
\end{frame}

% 16. 90 seconds. Include the counterexample to the strongest prose claim.
\begin{frame}{RL gains depend strongly on retrieved experience}
  \textbf{Qwen executor; trained Qwen curator unless marked base.}
  \medskip
  \centering
  \begin{tabular}{lrr}
  \toprule
  \textbf{Variant} & \textbf{ALFWorld SR} & \textbf{WebShop SR}\\
  \midrule
  Full \jit & \sr{77.4}{2.9} & \sr{32.8}{1.7}\\
  No retrieved trajectories & \sr{62.6}{1.2} & \sr{17.6}{1.1}\\
  No task conditioning & \sr{66.0}{4.7} & \sr{22.4}{0.4}\\
  Untrained \jit-base & \sr{60.5}{2.6} & \sr{11.7}{0.5}\\
  \bottomrule
  \end{tabular}
  \begin{block}{Supported conclusion}
    Retrieval removal costs 14.8 / 15.2 points. Some gains over the untrained model remain; the table supports a narrower claim than the prose.
  \end{block}
  \papersource{Paper, Sec. 4.2 and Fig. 2(b), p. 8; Table 9, p. 21. The paper does not fully specify retraining for every RL ablation.}
\end{frame}

% 17. 75 seconds. Refresh is confounded with 50 extra optimizer steps.
\begin{frame}{Bank refresh adds modest gains; warm-starting does not}
  \textbf{WebShop SR with the trained Qwen curator.}
  \medskip
  \centering
  \begin{tabular}{lrrr}
  \toprule
  \textbf{Test executor} & \textbf{Default} & \textbf{Refresh + 50 steps} & \textbf{Warm-start 100 traces}\\
  \midrule
  Qwen3-8B & 32.8 & 35.6 & 32.5\\
  Gemini-2.5-Pro & 50.5 & 50.5 & 50.9\\
  GPT-5.4 & 45.4 & 46.3 & 44.1\\
  \bottomrule
  \end{tabular}
  \medskip
  \begin{itemize}
    \item Refresh changes both the training bank and the training budget.
    \item No consistent positive effect from preloading the test bank.
    \item Early-task accuracy is not broken out, so cold-start costs remain unclear.
  \end{itemize}
  \papersource{Paper, Table 5, p. 8; discussion on pp. 8--9. Values are means; complete uncertainty is in the notes.}
\end{frame}

% 18. 75 seconds. Reconstruct the qualitative lesson with readable text.
\begin{frame}{Qualitative examples reveal procedural guidance}
  \begin{block}{Task: examine a bowl with a desk lamp}
    \begin{tabular}{p{5.5cm}p{6.0cm}}
    \textbf{Untrained curator} & \textbf{RL-trained curator}\\[2mm]
    Find the bowl; issue a generic examination action. &
    Take the bowl, move to the desk lamp, and examine the bowl using it.\\
    \end{tabular}
  \end{block}
  \begin{itemize}
    \item Fig. 3: one past trace yields state-change or placement guidance.
    \item Appendix B: shopping attributes and telecom diagnostic order are reused across instances.
    \item Examples illustrate behavior; they do not guarantee that every generated instruction is correct.
  \end{itemize}
  \papersource{Paper, Figs. 3--4, pp. 9--10; example payloads, pp. 22--24. Qualitative comparison paraphrased for readability.}
\end{frame}

% 19. 90 seconds. Separate the authors' limitations from reader observations.
\begin{frame}{Several boundaries matter for interpretation}
  \begin{columns}[T,onlytextwidth]
  \begin{column}{0.47\textwidth}
    \begin{block}{Authors' limitations}
      \begin{itemize}
        \item Simple BM25 retrieval.
        \item An extra curator call per task.
        \item Benchmark-specific payload format.
        \item Curation only once per task.
      \end{itemize}
    \end{block}
  \end{column}
  \begin{column}{0.49\textwidth}
    \begin{block}{Reading observations}
      \begin{itemize}
        \item No full-system cost comparison.
        \item Judge accuracy is not quantified.
        \item Some baseline values are inherited.
        \item 3--4 orderings are not independent training-seed evidence.
      \end{itemize}
    \end{block}
  \end{column}
  \end{columns}
  \papersource{Paper, Sec. 5, p. 10; Table 4, p. 7; baseline reproduction, Table 6, p. 18. Right column is reader analysis.}
\end{frame}

% 20. 75 seconds. End the main discussion with a design decision and testable next step.
\begin{frame}{Defer abstraction when future uses are uncertain}
  \begin{enumerate}
    \item \textbf{Architecture:} store accepted raw experience, then curate a temporary task-specific briefing.
    \medskip
    \item \textbf{Learning:} same-task reward trains a modular curator without waiting for later tasks.
    \medskip
    \item \textbf{Evidence:} strong benchmark gains and executor transfer, with important model and protocol dependence.
  \end{enumerate}
  \begin{block}{A practical follow-up experiment}
    Compare raw retrieval, prompted curation, and RL curation under the same total token and latency budget.
  \end{block}
  \papersource{Synthesis of paper Secs. 3--5, pp. 4--10. Follow-up experiment is a reader proposal, not a reported result.}
\end{frame}

\begin{frame}{Source and reading scope}
  \textbf{Yefan Zhou, Yang Li, Zeyu Leo Liu, Semih Yavuz, Shafiq Joty.}
  \medskip

  \emph{Just-in-Time Memory: Learning to Curate Task-Adaptive Memory for LLM Agents.}
  Salesforce AI Research, 2026.
  \medskip

  \href{https://arxiv.org/abs/2609.27334v1}{\texttt{https://arxiv.org/abs/2609.27334v1}}
  \begin{itemize}
    \item Read the author TeX, all main sections, and Appendices A/B.
    \item Checked key equations, tables, figures, and locators against the 25-page PDF.
    \item Figures reuse author assets; tables select and reformat reported values.
    \item This presentation analyzes the paper; it does not reproduce its experiments.
  \end{itemize}
  \papersource{Companion Chinese notes contain full explanations, uncertainty, and source locators. PDF page numbers match printed pages in v1.}
\end{frame}

\appendix

% 22. Backup. Clarify the difference between the displayed objective and implementation.
\begin{frame}{Backup: the implementation includes clipping and KL}
  \centering
  \begin{tabular}{p{5.6cm}p{7.0cm}}
  \toprule
  \textbf{Setting} & \textbf{Reported value}\\
  \midrule
  Curator / training executor & Qwen3-8B, non-thinking\\
  Updates / batch / group & 100 / 32 prompts / 8 samples\\
  Learning rate / warmup & $10^{-6}$ / 5 steps\\
  Clipping / loss aggregation & $(0.2,0.2)$ / token-mean\\
  KL loss & Enabled, coefficient $10^{-3}$\\
  KL in reward & Disabled\\
  Prompt / response cap & 32768 / 8192 (ALFWorld), 4096 (WebShop)\\
  Hardware / reported run time & 8 H200s; about 21 h / 27 h\\
  \bottomrule
  \end{tabular}
  \papersource{Paper, Appendix A and Table 7, PDF pp. 18--19. Test-time Qwen executor: thinking on ALFWorld, non-thinking on WebShop.}
\end{frame}

% 23. Backup. Report provenance without assuming every baseline was rerun.
\begin{frame}{Backup: no-memory baseline provenance needs care}
  \textbf{Qwen3-8B, reported vs. reproduced means.}
  \medskip
  \centering
  \begin{tabular}{lrrr}
  \toprule
  \textbf{Source} & \textbf{ALFWorld SR} & \textbf{WebShop score} & \textbf{WebShop SR}\\
  \midrule
  SkillOS reported / main Table 1 & 47.9 & 33.3 & 9.8\\
  Reproduced, non-thinking & 34.5 & 36.4 & 8.6\\
  Reproduced, thinking & 42.1 & 29.0 & 4.4\\
  \bottomrule
  \end{tabular}
  \medskip
  \begin{itemize}
    \item Executor prompts and thinking settings were calibrated during reproduction.
    \item A stronger reported SR is conservative for that gap, but not proof of identical configurations.
    \item Table 6's ``all at or below'' description has a score exception: 36.4 exceeds 33.3.
  \end{itemize}
  \papersource{Paper, Tables 1 and 6, PDF pp. 6 and 18. Values are transcribed from the paper; observations are reader analysis.}
\end{frame}

% 24. Backup. Avoid treating standard deviation as a significance threshold.
\begin{frame}{Backup: sensitivity claims have a limited scope}
  \textbf{WebShop SR, number of retrieved traces.}\par
  \medskip
  \centering
  \begin{tabular}{lrr}
  \toprule
  \textbf{Executor} & $\boldsymbol{k=3}$ & $\boldsymbol{k=5}$\\
  \midrule
  Qwen3-8B & \sr{32.8}{1.7} & \sr{32.8}{0.3}\\
  Gemini-2.5-Pro & \sr{50.5}{0.8} & \sr{48.6}{0.7}\\
  GPT-5.4 & \sr{45.4}{0.0} & \sr{45.3}{0.8}\\
  \bottomrule
  \end{tabular}
  \medskip
  \begin{itemize}
    \item Evidence covers two small $k$ values; larger banks are not tested here.
    \item Warm-starting GPT yields 44.1$\pm$0.4 vs. 45.4$\pm$0.0 by default.
    \item That 1.3-point gap is not literally within the displayed SDs; a paired significance test is not reported.
  \end{itemize}
  \papersource{Paper, Table 8, p. 20; Table 5, p. 8 and prose on p. 9. Mean $\pm$ SD; no confidence intervals are implied.}
\end{frame}

% 25. Backup. Curves are descriptive, not training-seed confidence intervals.
\begin{frame}{Backup: ALFWorld improves over 100 training steps}
  \centering
  \includegraphics[width=0.88\linewidth,height=0.70\textheight,keepaspectratio]{figures/fig-05-training-alfworld.pdf}
  \par\smallskip
  Top: training. Bottom: validation. Success rises and executor turns fall overall.
  \papersource{Original paper Fig. 5, PDF p. 24. Validation uses a subsampled test stream; its role in model selection is not fully specified.}
\end{frame}

% 26. Backup. Do not read the late-stage fluctuations as strict monotonic improvement.
\begin{frame}{Backup: WebShop improves with visible fluctuations}
  \centering
  \includegraphics[width=\linewidth,height=0.65\textheight,keepaspectratio]{figures/fig-06-training-webshop.pdf}
  \par\medskip
  Columns: success rate, average score, executor turns.\\
  Rows: training and validation. Trends are not strictly monotonic.
  \papersource{Original paper Fig. 6, PDF p. 25. These plots do not quantify variability across independent RL training runs.}
\end{frame}

\end{document}