diff --git a/optax/experimental/README.md b/optax/experimental/README.md
new file mode 100644
index 00000000..2251023e
--- /dev/null
+++ b/optax/experimental/README.md
@@ -0,0 +1,3 @@
+As the name suggests, the functions implemented here are subject to
+modifications. We are currently developing a new Solver API that could
+span more optimizers such as the ones using some linesearches.
diff --git a/optax/experimental/api_test.ipynb b/optax/experimental/api_test.ipynb
new file mode 100644
index 00000000..103a44f2
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+++ b/optax/experimental/api_test.ipynb
@@ -0,0 +1,58 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vfWSk55u5_E-"
+      },
+      "source": [
+        "# Example of Solver API usage\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "vzyIF6NW6Dwd"
+      },
+      "outputs": [],
+      "source": [
+        "import jax.numpy as jnp\n",
+        "import optax\n",
+        "from optax.experimental import gradient_solver\n",
+        "\n",
+        "def obj_fun(x):\n",
+        "  return jnp.sum(x**2)\n",
+        "\n",
+        "init, step = gradient_solver.gradient_solver(\n",
+        "    obj_fun, optax.adam(learning_rate=1.)\n",
+        "    )\n",
+        "\n",
+        "params = jnp.arange(16, dtype=jnp.float32)\n",
+        "state = init(params)\n",
+        "for _ in range(10):\n",
+        "  params, state = step(params, state)\n",
+        "  print(f'Objective value: {obj_fun(params)}')"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "last_runtime": {
+        "build_target": "//learning/grp/tools/ml_python:ml_notebook",
+        "kind": "private"
+      },
+      "private_outputs": true,
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/optax/experimental/gradient_solver.py b/optax/experimental/gradient_solver.py
new file mode 100644
index 00000000..2fcdc2cc
--- /dev/null
+++ b/optax/experimental/gradient_solver.py
@@ -0,0 +1,64 @@
+# Copyright 2023 DeepMind Technologies Limited. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Wraps a GradientTransform into a Solver."""
+
+from typing import Any, NamedTuple, Union
+
+import jax
+import optax
+import optax.experimental.solver as optax_solver
+import optax.experimental.utils as exp_utils
+
+
+class GradientSolverState(NamedTuple):
+  gt_state: optax.OptState = None
+
+
+def gradient_solver(obj_fn, gradient_transform, obj_fun_has_aux=False):
+  """Wraps a GradientTransform into a Solver."""
+
+  def init(init_params: optax.Params) -> optax_solver.SolverState:
+    init_gt_state = gradient_transform.init(init_params)
+    init_opt_state = GradientSolverState(init_gt_state)
+    return init_opt_state
+
+  def step(
+      params: optax.Params,
+      state: optax_solver.SolverState,
+      **extra_kwargs: dict[str, Any]
+  ) -> tuple[
+      Union[optax.Params, tuple[optax.Params, Any]], optax_solver.SolverState
+  ]:
+    obj_kwargs, gt_kwargs = exp_utils.split_kwargs(
+        (obj_fn, gradient_transform.update), extra_kwargs
+    )
+    if obj_fun_has_aux:
+      grad, aux = jax.grad(obj_fn, has_aux=obj_fun_has_aux)(
+          params, **obj_kwargs
+      )
+    else:
+      grad = jax.grad(obj_fn)(params, **obj_kwargs)
+      aux = None
+    update, gt_state = gradient_transform.update(
+        grad, state.gt_state, params, **gt_kwargs
+    )
+    next_params = optax.apply_updates(params, update)
+    next_state = GradientSolverState(gt_state)
+    if obj_fun_has_aux:
+      return (next_params, aux), next_state
+    else:
+      return next_params, next_state
+
+  return optax_solver.Solver(init, step)
diff --git a/optax/experimental/gradient_solver_test.py b/optax/experimental/gradient_solver_test.py
new file mode 100644
index 00000000..1ff0473e
--- /dev/null
+++ b/optax/experimental/gradient_solver_test.py
@@ -0,0 +1,110 @@
+# Copyright 2023 DeepMind Technologies Limited. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for `alias.py`."""
+
+from absl.testing import absltest
+from absl.testing import parameterized
+
+import chex
+import jax
+import jax.numpy as jnp
+
+from optax._src import alias
+from optax._src import numerics
+from optax.experimental import gradient_solver
+
+
+_GRAD_TRANSFORMS_UNDER_TEST = (
+    dict(gt_name='sgd', gt_kwargs=dict(learning_rate=1e-3, momentum=0.9)),
+    dict(gt_name='adafactor', gt_kwargs=dict(learning_rate=5e-3)),
+    dict(gt_name='adagrad', gt_kwargs=dict(learning_rate=1.0)),
+    dict(gt_name='adam', gt_kwargs=dict(learning_rate=1e-1)),
+    dict(gt_name='adamw', gt_kwargs=dict(learning_rate=1e-1)),
+    dict(gt_name='adamax', gt_kwargs=dict(learning_rate=1e-1)),
+    dict(gt_name='adamaxw', gt_kwargs=dict(learning_rate=1e-1)),
+    dict(gt_name='amsgrad', gt_kwargs=dict(learning_rate=1e-1)),
+    dict(gt_name='lars', gt_kwargs=dict(learning_rate=1.0)),
+    dict(gt_name='lamb', gt_kwargs=dict(learning_rate=1e-3)),
+    dict(
+        gt_name='lion', gt_kwargs=dict(learning_rate=1e-2, weight_decay=1e-4),
+    ),
+    dict(gt_name='nadam', gt_kwargs=dict(learning_rate=1e-2)),
+    dict(gt_name='nadamw', gt_kwargs=dict(learning_rate=1e-2)),
+    dict(gt_name='noisy_sgd', gt_kwargs=dict(learning_rate=1e-3, eta=1e-4)),
+    dict(gt_name='novograd', gt_kwargs=dict(learning_rate=1e-3)),
+    dict(
+        gt_name='optimistic_gradient_descent',
+        gt_kwargs=dict(learning_rate=2e-3, alpha=0.7, beta=0.1),
+    ),
+    dict(gt_name='rmsprop', gt_kwargs=dict(learning_rate=5e-3)),
+    dict(gt_name='rmsprop', gt_kwargs=dict(learning_rate=5e-3, momentum=0.9)),
+    dict(gt_name='fromage', gt_kwargs=dict(learning_rate=5e-3)),
+    dict(gt_name='adabelief', gt_kwargs=dict(learning_rate=1e-2)),
+    dict(gt_name='radam', gt_kwargs=dict(learning_rate=5e-3)),
+    dict(gt_name='rprop', gt_kwargs=dict(learning_rate=1e-1)),
+    dict(gt_name='sm3', gt_kwargs=dict(learning_rate=1.0)),
+    dict(gt_name='yogi', gt_kwargs=dict(learning_rate=1e-1)),
+)
+
+
+def _setup_parabola(dtype):
+  """Quadratic function as an optimization target."""
+  initial_params = jnp.array([-1.0, 10.0, 1.0], dtype=dtype)
+  final_params = jnp.array([1.0, -1.0, 1.0], dtype=dtype)
+
+  def obj_fun(params):
+    return jnp.sum(numerics.abs_sq(params - final_params))
+
+  return initial_params, final_params, obj_fun
+
+
+def _setup_rosenbrock(dtype):
+  """Rosenbrock function as an optimization target."""
+  a = 1.0
+  b = 100.0
+
+  initial_params = jnp.array([0.0, 0.0], dtype=dtype)
+  final_params = jnp.array([a, a**2], dtype=dtype)
+
+  def obj_fun(params):
+    return (numerics.abs_sq(a - params[0]) +
+            b * numerics.abs_sq(params[1] - params[0]**2))
+
+  return initial_params, final_params, obj_fun
+
+
+class SolverWrapperTest(chex.TestCase):
+
+  @parameterized.product(
+      _GRAD_TRANSFORMS_UNDER_TEST,
+      target=(_setup_parabola, _setup_rosenbrock),
+      dtype=(jnp.float32,),
+  )
+  def test_optimization(self, gt_name, gt_kwargs, target, dtype):
+    opt = getattr(alias, gt_name)(**gt_kwargs)
+    initial_params, final_params, obj_fun = target(dtype)
+
+    init, step = gradient_solver.gradient_solver(obj_fun, opt)
+
+    params = initial_params
+    state = init(params)
+    step = jax.jit(step)
+    for _ in range(10_000):
+      params, state = step(params, state)
+
+    chex.assert_trees_all_close(params, final_params, rtol=3e-2, atol=3e-2)
+
+if __name__ == '__main__':
+  absltest.main()
diff --git a/optax/experimental/solver.py b/optax/experimental/solver.py
new file mode 100644
index 00000000..1fda3393
--- /dev/null
+++ b/optax/experimental/solver.py
@@ -0,0 +1,89 @@
+# Copyright 2023 DeepMind Technologies Limited. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Solver API."""
+
+from typing import Any, NamedTuple, Protocol, Union
+
+import optax
+
+Params = optax.Params
+SolverState = Any
+
+
+class SolverInitFn(Protocol):
+  """A callable type for the `init` function of a `Solver`.
+
+  The `init` function takes a tree of `params` and uses these to construct an
+  arbitrary structured initial `state` for the solver. This
+  may hold statistics of the past updates or any other non static information.
+  """
+
+  def __call__(self, params: Params) -> SolverState:
+    """Initialize the solver.
+
+    Args:
+      params: The initial value of the parameters.
+
+    Returns:
+      The initial state of the solver.
+    """
+
+
+class SolverStepFn(Protocol):
+  """A callable type for the `step` function of a `Solver`.
+
+  The `step` function takes a tree of candidate parameters `params`, and an
+  arbitrary structured `state` to return a new tree of candidate parameters,
+  and a new state. Additional arguments can be fed in a keyword format.
+  """
+
+  def __call__(
+      self, params: Params, state: SolverState, **extra_kwargs: dict[str, Any]
+  ) -> tuple[Union[Params, tuple[Params, Any]], SolverState]:
+    """Performs a step of the solver.
+
+    Args:
+      params: A tree of candidate parameters.
+      state: The state of the solver.
+      **extra_kwargs: Additional arguments for the function or the solver in
+        keyword format.
+
+    Returns:
+      The updated parameters, eventually with an auxiliary output,
+      and updated state.
+    """
+
+
+class Solver(NamedTuple):
+  """A pair of pure functions implementing a solver.
+
+  The init function initializes the state of the solver given an initial tree of
+  parameters. The step function updates the parameters and the state of the 
+  solver given current parameters and state.
+  Contrarily to GradientTransformation, this API accesses the function to be
+  optimized directly to compute gradients, then update directions and finally
+  updated parameters.
+
+  Attributes:
+    init: A pure function which, when called with an example instance of the
+      parameters, returns an arbitrary structured initial `state`.
+    step: A pure function which takes as input a tree of parameters, the
+      previous solver state (which may have been initialized using the init
+      function). The step function then returns the updated parameters,
+      and a new solver state.
+  """
+
+  init_fn: SolverInitFn
+  step_fn: SolverStepFn
diff --git a/optax/experimental/utils.py b/optax/experimental/utils.py
new file mode 100644
index 00000000..5f386358
--- /dev/null
+++ b/optax/experimental/utils.py
@@ -0,0 +1,65 @@
+# Copyright 2023 DeepMind Technologies Limited. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities for solvers.
+"""
+import functools
+import inspect
+import operator
+
+from typing import Any, Callable, Sequence
+
+
+def split_kwargs(
+    funs: Sequence[Callable[..., Any]],
+    fun_kwargs: dict[str, Any],
+) -> Sequence[dict[str, Any]]:
+  """Split fun_kwargs into kwargs of the input functions funs.
+
+  Raises an error in one keyword argument of fun_kwargs does not match any
+  argument name of funs.
+
+  Args:
+    funs: sequence of functions to feed fun_kwargs to
+    fun_kwargs: dictionary of keyword variables to be fed to funs
+
+  Returns:
+    (fun_1_kwargs, ..., fun_n_kwargs): keyword arguments for each function taken
+      from fun_kwargs.
+
+  Examples:
+    >>> def fun1(a, b): return a+b
+    >>> def fun2(c, d): return c+d
+    >>> fun_kwargs = {'b':1., 'd':2.}
+    >>> funs_kwargs = split_kwargs((fun1, fun2), fun_kwargs)
+    >>> print(funs_kwargs)
+    [{'b': 1.0}, {'d': 2.0}]
+  """
+  funs_arg_names = [
+      list(inspect.signature(fun).parameters.keys()) for fun in funs
+  ]
+  funs_kwargs = [
+      {k: v for k, v in fun_kwargs.items() if k in fun_arg_names}
+      for fun_arg_names in funs_arg_names
+  ]
+  all_possible_arg_names = functools.reduce(operator.add, funs_arg_names)
+  remaining_keys = [
+      k for k in fun_kwargs.keys() if k not in all_possible_arg_names
+  ]
+  if remaining_keys:
+    raise ValueError(
+        f'{remaining_keys} are not valid arguments for any of the functions'
+        f' {funs}'
+    )
+  return funs_kwargs