\begin{algorithmic}[1]
\State 
Set $\mathcal{D} \gets \emptyset$ \Comment{{\it Initialize dataset.}}
\For{$t=1,\ldots, T$} \Comment{{\it Training $T$ rounds}}
    \State $\beta_1,\ldots,\beta_M \sim P_{\text{exp}}(\beta)$ \Comment{{\it Sample temperatures from exploration query prior.}}
    \For{$m=1,\ldots,M$}
    \State $\tau_m \sim P_F(\tau|\beta = \beta_m;\theta)$ \Comment{{\it Sample trajectories from Logit-GFN.}}
    \State $\mathcal{D} \gets \mathcal{D} \cup \{\tau_m\}$ 
    \EndFor
    \For{$k = 1, \ldots K$} \Comment{{\it Training $K$ epochs per each training rounds}}
        \State Use ADAM for gradually minimizing $\mathcal{L}(\theta;\mathcal{D})$.
    \EndFor
\EndFor
\State Output: $\mathcal{D}$
\end{algorithmic}

\begin{algorithmic}
[1]
\State
Set $\mathcal{D} \gets \emptyset$ \Comment{{\it Initialize dataset.}}
\For{$t=1,\ldots, T$} \Comment{{\it Training $T$ rounds}}
\State $\beta_1,\ldots,\beta_M \sim P_{\text{exp}}(\beta)$ \Comment{{\it Sample temperatures from exploration query prior.}}
\For{$m=1,\ldots,M$}
\State $\tau_m \sim P_F(\tau|\beta = \beta_m;\theta)$ \Comment{{\it Sample trajectories from Logit-GFN.}}
\State $\mathcal{D} \gets \mathcal{D} \cup \{\tau_m\}$
\EndFor
\For{$k = 1, \ldots K$} \Comment{{\it Training $K$ epochs per each training rounds}}
\State Use ADAM for gradually minimizing $\mathcal{L}(\theta;\mathcal{D})$.
\EndFor
\EndFor
\State Output: $\mathcal{D}$
\end{algorithmic}
{
    "title": "learning to scale logits for temperature-conditional gflownets",
    "id": "2310.02823",
    "abstract": "gflownets are probabilistic models that sequentially generate compositional structures through a stochastic policy. among gflownets, temperature-conditional gflownets can introduce temperature-based controllability for exploration and exploitation. we propose \\textit{logit-scaling gflownets} (logit-gfn), a novel architectural design that greatly accelerates the training of temperature-conditional gflownets. it is based on the idea that previously proposed approaches introduced numerical challenges in the deep network training, since different temperatures may give rise to very different gradient profiles as well as magnitudes of the policy's logits. we find that the challenge is greatly reduced if a learned function of the temperature is used to scale the policy's logits directly. also, using logit-gfn, gflownets can be improved by having better generalization capabilities in offline learning and mode discovery capabilities in online learning, which is empirically verified in various biological and chemical tasks. our code is available at \\url{https://github.com/dbsxodud-11/logit-gfn}",
    "categories": "cs.lg stat.ml",
    "doi": "",
    "created": "2023-10-04",
    "updated": "2024-02-04",
    "authors": [
        "minsu kim",
        "joohwan ko",
        "taeyoung yun",
        "dinghuai zhang",
        "ling pan",
        "woochang kim",
        "jinkyoo park",
        "emmanuel bengio",
        "yoshua bengio"
    ],
    "affiliation": [],
    "url": "https://arxiv.org/abs/2310.02823"
}
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