diff --git a/docs/evidence/spinningup_research_source_graph.md b/docs/evidence/spinningup_research_source_graph.md
deleted file mode 100644
index 93ffbb8..0000000
--- a/docs/evidence/spinningup_research_source_graph.md
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@@ -1,45 +0,0 @@
-# Spinning Up as a Deep RL Researcher - source graph and research-taste excerpts
-
-Primary source: https://spinningup.openai.com/en/latest/spinningup/spinningup.html
-Author: Joshua Achiam, OpenAI
-Date: October 13th, 2018
-Related local cache: docs/evidence/spinningup_researcher.md
-Fetch-status: index only. The full page text now lives in docs/evidence/spinningup_researcher.md (fetched 2026-08-15); this file keeps the source graph so the two caches do not hold the same 3.3k words twice (CLAUDE agent)
-Use: RL research-process evidence, especially for source graph, fair comparisons, seeds, preregistration, and ablations.
-
-## Why this matters for agents
-
-Spinning Up is not just an RL textbook page. Its researcher page is a compact research apprenticeship guide. It points to the same battle-tested debugging and reproducibility references already cached in this repo, then adds project selection and rigorous comparison advice.
-
-## Quotes
-
-The quotes that used to sit here (simplicity is critical, iterate fast in simple
-environments, avoid reinventing the wheel, handicap the baseline, seeds,
-preregistration, ablations) are all in the full page text at
-[spinningup_researcher.md](spinningup_researcher.md). Read that file for the
-wording; this one only carries the source graph. (CLAUDE agent, 2026-08-15)
-
-## Source graph
-
-Spinning Up intro references, with local status:
-- Alex Irpan, Deep Reinforcement Learning Doesn't Work Yet: https://www.alexirpan.com/2018/02/14/rl-hard.html. Local cache: docs/evidence/alexirpan_rl_hard.md.
-- Islam et al., Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control: https://arxiv.org/pdf/1708.04133. Not separately cached; discussed/cited inside Henderson local cache.
-- Henderson et al., Deep Reinforcement Learning that Matters: https://arxiv.org/pdf/1709.06560. Local cache: docs/evidence/henderson_2018_deep_rl_matters.md.
-- Matthew Rahtz, Lessons Learned Reproducing a Deep RL Paper: http://amid.fish/reproducing-deep-rl. Local cache: docs/evidence/amid_fish_reproducing_deep_rl.md.
-- David Silver UCL RL course: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html. Not cached.
-- Berkeley Deep RL course: http://rll.berkeley.edu/deeprlcourse/. Not cached.
-- Deep RL Bootcamp lectures: https://sites.google.com/view/deep-rl-bootcamp/lectures. Reddit index cache: docs/evidence/reddit_deeprl_bootcamp_2017_75m5vd.md.
-- John Schulman, Nuts and Bolts of Deep RL: http://joschu.net/docs/nuts-and-bolts.pdf. Local cache: docs/evidence/joschu_nuts_and_bolts.md.
-- Tim Rocktaschel et al., Advice for Short-term Machine Learning Research Projects: https://rockt.github.io/2018/08/29/msc-advice.html. Not cached.
-- Catherine Olsson / 80,000 Hours, ML Engineering for AI Safety & Robustness: https://80000hours.org/articles/ml-engineering-career-transition-guide/. Not cached.
-
-## Likely follow-up cache candidates
-
-Priority 1:
-- Chris Olah, research taste: short and directly named by Nanda.
-- Jacob Steinhardt, Research as a Stochastic Decision Process: directly named by Nanda for prioritization.
-- Tim Rocktaschel et al., short-term ML research projects: directly named by Spinning Up for research growth.
-
-Priority 2:
-- David Silver/UCL, Berkeley Deep RL, Deep RL Bootcamp: curriculum material, less directly research-taste except via RL mastery.
-- Catherine Olsson/80k: career/field-entry framing; useful if the skill expands beyond project-level research taste.
diff --git a/refs/research_taste.md b/refs/research_taste.md
index 76b3121..f70e79f 100644
--- a/refs/research_taste.md
+++ b/refs/research_taste.md
@@ -216,7 +216,7 @@ Less central but useful:
 [^nanda-paper]: Neel Nanda, "Highly Opinionated Advice on How to Write ML Papers" (2025-05-12) - https://www.lesswrong.com/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-papers ([cache](../docs/evidence/nanda_highly_opinionated_ml_paper_writing.md)).
 [^olah-taste]: Chris Olah, "Research Taste Exercises" (2021-01-09) - https://colah.github.io/notes/taste/ ([cache](../docs/evidence/olah_research_taste_exercises.md)).
 [^steinhardt]: Jacob Steinhardt, "Research as a Stochastic Decision Process" - https://cs.stanford.edu/~jsteinhardt/ResearchasaStochasticDecisionProcess.html ([cache](../docs/evidence/steinhardt_research_stochastic_decision_process.md)).
-[^spinningup]: Joshua Achiam, "Spinning Up as a Deep RL Researcher" (OpenAI, 2018-10-13) - https://spinningup.openai.com/en/latest/spinningup/spinningup.html ([research cache](../docs/evidence/spinningup_research_source_graph.md), [debugging cache](../docs/evidence/spinningup_researcher.md)).
+[^spinningup]: Joshua Achiam, "Spinning Up as a Deep RL Researcher" (OpenAI, 2018-10-13) - https://spinningup.openai.com/en/latest/spinningup/spinningup.html ([cache](../docs/evidence/spinningup_researcher.md)).
 [^rahtz]: Matthew Rahtz, "Lessons Learned Reproducing a Deep Reinforcement Learning Paper" (2018) - http://amid.fish/reproducing-deep-rl ([cache](../docs/evidence/amid_fish_reproducing_deep_rl.md)).
 [^henderson]: Henderson et al., "Deep Reinforcement Learning that Matters" (AAAI 2018) - https://arxiv.org/pdf/1709.06560 ([cache](../docs/evidence/henderson_2018_deep_rl_matters.md)).
 [^schulman]: John Schulman, "Nuts and Bolts of Deep RL Research" (2016) - http://joschu.net/docs/nuts-and-bolts.pdf ([cache](../docs/evidence/joschu_nuts_and_bolts.md)).