update readme

README.md

@@ -251,12 +251,17 @@ For discrete environments, a `Tabular` module is provided, where a lookup table
 
 ### Samplers
 
-A [Sampler](https://github.com/saleml/torchgfn/tree/master/src/gfn/samplers.py) object defines how actions are sampled (`sample_actions()`) at each state, and trajectories  (`sample_trajectories()`), which can sample a batch of trajectories starting from a given set of initial states or starting from $s_0$. It requires a `GFNModule` that implements the `to_probability_distribution` function. For off-policy sampling, the parameters of `to_probability_distribution` can be directly passed when initializing the `Sampler`.
+A [Sampler](https://github.com/saleml/torchgfn/tree/master/src/gfn/samplers.py) object defines how actions are sampled (`sample_actions()`) at each state, and trajectories  (`sample_trajectories()`), which can sample a batch of trajectories starting from a given set of initial states or starting from $s_0$. It requires a `GFNModule` that implements the `to_probability_distribution` function. For simple off-policy sampling (e.g., epsilon-noisy or tempering), you can pass appropriate `policy_kwargs` to the `Sampler` object, which will be used by the `GFNModule`. If you need more complex off-policy sampling, you can subclass the `Sampler` object, and override the `sample_actions` and `sample_trajectories` methods.
+
+Currently, the library provides two samplers:
+
+- Sampler
+- LocalSearchSampler (references: [EB-GFN](https://arxiv.org/abs/2202.01361), [LS-GFN](https://arxiv.org/abs/2310.02710))
 
 
 ### Losses
 
-GFlowNets can be trained with different losses, each of which requires a different parametrization, which we call in this library a `GFlowNet`. A `GFlowNet` is a `GFNModule` that includes one or multiple `GFNModule`s, at least one of which implements a `to_probability_distribution` function. They also need to implement a `loss` function, that takes as input either states, transitions, or trajectories, depending on the loss.
+GFlowNets can be trained with different losses, each of which requires a different parametrization, which we call in this library a `GFlowNet`. A `GFlowNet` includes one or multiple `GFNModule`s, at least one of which implements a `to_probability_distribution` function. They also need to implement a `loss` function, that takes as input either states, transitions, or trajectories, depending on the loss.
 
 Currently, the implemented losses are: