Systematically optimize agentic editing performance (#28961)

Now that we've established a proper eval in tree, this PR is reboots of
our agent loop back to a set of minimal tools and simpler prompts. We
should aim to get this branch feeling subjectively competitive with
what's on main and then merge it, and build from there.

Let's invest in our eval and use it to drive better performance of the
agent loop. How you can help: Pick an example, and then make the outcome
faster or better. It's fine to even use your own subjective judgment, as
our evaluation criteria likely need tuning as well at this point. Focus
on making the agent work better in your own subjective experience first.
Let's focus on simple/practical improvements to make this thing work
better, then determine how we can craft our judgment criteria to lock
those improvements in.

Release Notes:

- N/A

---------

Co-authored-by: Max <max@zed.dev>
Co-authored-by: Antonio <antonio@zed.dev>
Co-authored-by: Agus <agus@zed.dev>
Co-authored-by: Richard <richard@zed.dev>
Co-authored-by: Max Brunsfeld <maxbrunsfeld@gmail.com>
Co-authored-by: Antonio Scandurra <me@as-cii.com>
Co-authored-by: Michael Sloan <mgsloan@gmail.com>

crates/eval/examples/docs_restructure/diff_criteria.md

@@ -0,0 +1,12 @@
+1. README.md Features Section Reorganization
+The features section has been reorganized into two subsections ("Baselines" and "Games") with markdown tables added. The previous bullet points were replaced with more structured content including supported/benchmarked status indicators. A new "Visualization" section was added with TensorBoard and port forwarding instructions.
+2. Content Relocation and File Restructuring
+The Tennis game documentation and action space details were moved from README.md to a new games.md file. The README was cleaned up by removing commented-out content and consolidating documentation sections. YAML config files (Benchmark-2T1P-Discrete.yaml and Test-Pong.yaml) were modified to replace `selfplay_recent_prob` with `playing_policy_load_recent_prob` and adjust population size options.
+3. train.py Refactoring
+Significant changes to train.py including:
+- Renamed `selfplay_recent_prob` parameter to `playing_policy_load_recent_prob`
+- Simplified the nested grid search structure by removing unnecessary loops
+- Improved policy loading logic with better checkpoint path handling
+- Enhanced error handling and logging for policy saving/reloading
+- Removed redundant code and improved code organization
+- Added more descriptive console output during policy operations