v2.13.0: ReGraMa dormant-neuron resets

Parameter mutation now resets dormant neurons before the Gaussian pass, using ReGraMa (gradient-magnitude
scoring). The old amplified (“super”) Gaussian band is gone.

Features

• ReGraMa runs as the first stage of every parameter mutation. It scores each neuron with the GraMa metric of
  Liu et al., “Measure gradients, not activations!” — mean absolute gradient of
  the loss w.r.t. the pre-activation, normalised by that layer’s mean. Neurons at or below dormant_threshold
  (default 0.01) are reset: Xavier-uniform incoming weights, zero bias, small non-zero outgoing weights, and any
  adjacent norm entry restored to the identity. Output layers of heads are never reset. Target / shared networks are
  re-synced afterwards.
• Capture rides the existing init_training_step / finalize_training_step pair, so on-policy, off-policy,
  multi-agent, bandit, and offline trainers all get scores with no extra forward/backward pass. LLM algorithms still
  skip parameter mutation.
• Sensitivity is one field on the existing mutation block:

  mutation:
      dormant_threshold: 0.01

  The same argument exists on Mutations(...). Existing manifests and constructor calls keep working.

Changes

• The Gaussian pass no longer has a “super” band (10× noise on ~5% of sampled weights). Of the 10% of weights
  sampled for mutation, 95% get ordinary noise scaled by mutation_sd and the weight’s own magnitude; 5%
  are redrawn from N(0, 1). The split is fixed.

CI

• Pushing a v* or agilerl-arena/v* tag no longer publishes to PyPI. Create a GitHub Release from an existing
  tag with workflow_dispatch on the Publish release workflow.

What's Changed

• ReGraMa & Amplified-Gaussian / Random-Reset Parameter Mutations Switches by @agilerl-hub-sync in
  #685
• ci: publish PyPI from workflow_dispatch only

Full Changelog: v2.12.0...v2.13.0