message: update results with links to new files
diff: @@ -125,7 +125,7 @@ left us in a local minima with 3-3s that D-noise & etc. are not able to
  overcome.
  -## Third run, Minigo, v250-..., Jan 20th ongoing
  +## Third run, Minigo, v250-..., Jan 20th-Feb 1st (ish)
  As our fiddling about after v250 didn't seem to get us anywhere, and due to our
  reconsidering about whether or not to one-hot the policy network target ('pi'), we rolled
  @@ -167,9 +167,35 @@ Results: After a few days a few troublesome signs appeared.
  These could also have been exacerbated by our computed value for the learning
  rate decay schedule being incorrect.
  -This run is halted while we:
  +This run was halted after discovering a pretty major bug in our huge Dataset
  +rewrite that left it training towards what was effectively noise. Read the gory
  +details
  +[here](https://github.com/tensorflow/minigo/commit/9c6e013293d415e90b92097327dfaca94a81a6da).
  +
  +Our [milestones](https://github.com/tensorflow/minigo/milestone/1?closed=1) to hit before running a new 19x19 pipeline:
  - add random transformations to the training pipeline
  - More completely shuffle our training data.
  - Figure out an equivalent learning rate decay schedule to the one described
  in the paper.
  +
  +### Fourth run, 9x9s. Feb 7-
  +
  +Since our 19x19 run seemed irrepairable, we decided to work on instrumentation
  +to better understand when things were going off the rails while it would still
  +be possible to make adjustments.
  +
  +These [improvements](https://github.com/tensorflow/minigo/milestone/2?closed=1) included:
  + - Logging the [magnitude of the
  + updates](https://github.com/tensorflow/minigo/commit/360e056f218833938d845b454b4e24158034b58a)
  + that training would make to our weights.
  + - [Setting aside a
  + set of positions](https://github.com/tensorflow/minigo/commit/f941f5ac72d860f1f583392cbeb69d0694373824) to use to detect
  +overfitting.
  + - Improvements to setting up clusters and setting up automated testing/CI.
  +
  +
  +The results were very promising, reaching pro strength after about a week.
  +
  +Our models and training data can be found in [the GCS bucket
  +here](https://console.cloud.google.com/storage/browser/minigo-pub/v3-9x9).
  