Changelog

hand-writtenSource on GitHub.

Notable changes to the repo. Not a release log (there are no releases), just a record of what moved and why.

2026-10-30: fix broken links in the diagrams collection and the math cheat sheet

Found by checking every link on a site built from this repo. Both files are still unreviewed; only the link targets and one bracket changed.

• notes/diagrams/RL-Algorithm-Diagrams.md. Eight of the twelve entries in its table of contents went nowhere: they pointed at shortened anchors (#5-actor-critic) instead of the headings’ real ones (#5-actor-critic-architecture). Each now matches its heading.
• notes/cheat-sheets/RL-Math-Formulas.md. S[π_θ](s_t), the entropy term under the PPO objective, rendered as a link to a file called s_t. The opening bracket is escaped, so it renders as written.

2026-10-29: fix lecture navigation, the all-exercises test command, stale counts, and the landscape diagram

Context: a review found that the lecture series couldn’t be followed end to end, pytest exercises/ didn’t work, and the README diagram placed several methods in the wrong family. Nothing was deleted; every lecture is still unreviewed.

• Next-lecture links. 20 lectures had a missing or wrong pointer. Lectures 20–34 were drafted in parallel, so several said the series ended or that the next lecture didn’t exist yet (“There’s no lecture 22 yet”, “This is the final numbered lecture”). Lecture 06 pointed at 10, skipping 09, which 10 depends on. Each now points to the next lecture in CURRICULUM.md’s order (01 → … → 19 → 20 → 21 → 22 → 32 → 33 → 23 → … → 28 → 34 → 29 → 30 → 31). Only the wrong sentence changed; the suggestions and checklists around it stayed. Checked by walking the chain from 01 to 31 and resolving every relative link in notes/lectures/.
• pytest exercises/. It failed at import for 8 of 9 exercises: each exercise names its module starter, and Python reused the first one it loaded. Added exercises/conftest.py, which drops the cached module before each test file is collected. All 84 tests now collect from the repo root (was 5 collected, 8 errors), and with each reference solution copied over its starter, all 84 pass in one pytest exercises/ run (about 5.5 minutes on a laptop CPU).
• Stale counts. readme.md said five exercises; there are nine. notes/README.md’s top list named two cheat sheets; it now points to the four newer ones. AGENTS.md said 19 lectures; there are 34.
• Lecture 27 misattribution. It called InstructGPT’s preference data “Anthropic’s” and put it at ~40k comparisons. InstructGPT is OpenAI’s, and the paper (arXiv:2203.02155, §3.2) gives the reward-model set as 33k training prompts, each with 4 to 9 responses ranked by a labeler. Fixed against the paper.
• Landscape diagram (notes/diagrams/svg/landscape.svg), content only, same layout:
  • GRPO was listed under “DPO & cousins”. It’s an online policy-gradient method and was already under RLVR; SimPO takes its slot.
  • AlphaCode and CodeRL were under RLHF. Neither uses human preference labels, so they moved to RLVR as “code: run the tests” (CodeRL uses test results as reward; AlphaCode uses tests to filter samples).
  • “Distillation (R1 → small models)” was under RLAIF. It’s supervised fine-tuning on a teacher’s outputs, so it moved to offline RL as “imitate the data”.
  • WebGPT was listed as a benchmark. It’s a model trained with human feedback, so it moved to a new “browsers & screens” row; WebArena takes the benchmark slot. “ToolFormer” is now “Toolformer”.
  • The offline column said its reward source was “none”. Offline datasets do carry rewards; it now says “rewards logged in a fixed dataset”.
  • Lectures 20–34 added to the lecture numbers under each family, plus a “beyond the basics” row under classical RL and an “AI feedback at scale” row under RLAIF.
  • The one-line takeaways under each family went from about 2.1:1 contrast to about 5.6:1. Text size is unchanged; the diagram still renders small at README width, which a redesign will need to fix.
  • The 8 em dashes are gone (the earlier sweep covered .md files only).

2026-05-15: promote the hand-written notes out of Archive/

The trusted, hand-written 2017 notes had been sitting in a folder called Archive/, which connotes “old/dead”, while the unreviewed AI-drafted lecture series occupied notes/. Backwards. Fixed:

• Archive/2017-Course-Notes/CS294-DeepRL-Berkeley/ → notes/cs294-2017/ (with imgs/ intact and image links unchanged).
• Archive/2017-Course-Notes/Elements-Of-RL/ → notes/sutton-barto-digest/.
• Both moved files got a <!-- status: hand-written --> header.
• Archive/ directory deleted (Archive/README.md was a wrapper; no content lost).
• Root readme.md “What’s here” section restructured to lead with the trusted, hand-written content (the CS294 notes, the Sutton & Barto digest, the curated talks/books/courses, the tested exercises) and clearly demote the AI-drafted lecture series as scaffold-with-skepticism. “Start here” reordered to lead with safer paths (talks/books → exercises → drafts).
• notes/README.md rewritten in the same spirit: hand-written content first, lecture series second with a clear caveat about what unreviewed means.
• AGENTS.md and CLAUDE.md updated: the layout table now points at notes/cs294-2017/ and notes/sutton-barto-digest/ as the trusted, frozen, never-reword material instead of Archive/.
• GitHub topics refreshed: dropped guideline and study (generic), added rlhf, llm-alignment, dpo, grpo, ppo, rlvr, agentic-rl, lecture-notes, study-notes, deepseek-r1, constitutional-ai, policy-gradient, q-learning, sutton-barto. Description sharpened.

2026-05-16: bulk add: 15 new lectures, 4 new exercises, 4 new cheat sheets, 2 new reading lists

Context: the lecture series stopped at 19 (offline RL) and was missing most of the 2024–2025 material: exploration, multi-agent, world models, the agentic / reasoning / RLAIF / reward-hacking deep-dives, the systems and hardware layer, and the meta-RL / robotics adjacencies. Twenty-five parallel agents wrote one chunk each, each to a unique path, each unreviewed. The orchestrator wired them into the index files; no existing material was reworded.

Lectures added (notes/lectures/, all unreviewed, ~10,200 lines total):

• 20: Exploration: ε-greedy → UCB → RND → ICM → NGU → Go-Explore, plus how exploration shows up differently in LLM-RL (temperature / best-of-N rather than dedicated bonuses).
• 21: Multi-agent RL and self-play: stochastic games, CTDE (MADDPG, QMIX, MAPPO), self-play (AlphaZero, AlphaStar, OpenAI Five), PSRO and exploitability.
• 22: World models: the Dreamer family (PlaNet → V3), MuZero / EfficientZero / Sampled MuZero, transformer world models (IRIS, GAIA-1, Genie), the LLM-as-world-model thread.
• 23: Process reward models vs outcome reward models: PRM800K, Math-Shepherd, why DeepSeek skipped PRMs, best-of-N re-ranking, the autoPRM line.
• 24: Computer use and browser agents: WebGPT → Mind2Web → WebArena → VisualWebArena → OSWorld; Set-of-Marks; Anthropic Computer Use and OpenAI Operator; prompt-injection failure modes.
• 25: Long-horizon credit assignment: GAE/PPO at long horizons, hindsight relabeling, Reflexion, ToT, MCTS-then-train, the open question of value functions at 1000+ steps.
• 26: RL for mathematical reasoning: GSM8K/MATH/AIME/OlympiadBench/MiniF2F; STaR; PRM800K; DeepSeekMath/GRPO; DeepSeek-R1; the rollout/verifier loop; tool-augmented math RL (PAL, Lean/Coq).
• 27: RLAIF and synthetic preferences: Lee et al RLAIF, OAIF, UltraFeedback; LLM-judge biases (position, length, self-preference, sycophancy) and mitigations; weak-to-strong generalization.
• 28: Reward hacking and verifier design: Goodhart, CoastRunners; Stiennon overoptimization, Gao scaling laws (KL as the controller); RLVR-specific verifier hacks (test-overfitting, format exploits, judge-injection); mitigations.
• 29: Distributed RL systems: A3C → IMPALA → Ape-X → SEED RL → R2D2; Ray RLlib, ACME; the LLM-RL stack (TRL, OpenRLHF, verl/HybridFlow, DeepSpeed-Chat) with vLLM rollouts and FSDP/ZeRO training.
• 30: RL inference infrastructure: why ~70–90% of LLM-RL wall-clock is decode; PagedAttention, continuous batching, RadixAttention; speculative decoding caveats for RL; weight-sync patterns.
• 31: Hardware for RL: classical (CPU-env-bottlenecked) vs LLM-RL (HBM-bandwidth-bound); accelerators (H100/H200/B200, MI300X, TPU v5p/Trillium); FlashAttention 1/2/3; Triton; FP8/INT4 quantization for rollouts; interconnect geometry.
• 32: Meta-RL and in-context RL: MAML and Reptile (gradient-based); RL² and “Learning to RL” (recurrence-based); Decision Transformer, Trajectory Transformer, Algorithm Distillation, DPT; the bridge to LLM in-context learning.
• 33: Robotics RL: PILCO, domain randomization, OpenAI dexterous hand; the data-driven shift (RT-1, RT-2, Octo, OpenVLA, π₀, Open X-Embodiment); residual policies; Isaac Gym, MuJoCo, Brax, Drake.
• 34: Self-distillation and self-improvement loops: STaR, ReST^EM, Self-Instruct, CAI’s RL phase, self-rewarding LMs; failure modes (mode collapse, hallucination amplification, self-preference); the connection to RLVR.

Cheat sheets added (notes/cheat-sheets/, all unreviewed):

• RLHF-vs-DPO-vs-GRPO.md: side-by-side of PPO-RLHF / DPO / IPO / KTO / ORPO / SimPO / GRPO / RLVR, with comparison table, per-method blocks, decision tree.
• RL-LLM-loops-2026.md: ASCII data-flow diagrams of every major LLM-RL training loop (SFT, RLHF, DPO, iterative DPO, RLVR/GRPO, CAI, R1-Zero, R1-distill, agentic).
• KL-control.md: KL penalties across TRPO/PPO/RLHF/DPO/GRPO with formulas, K1/K2/K3 estimators, β tuning rules of thumb.
• RL-loss-functions.md: one block per algorithm (23 total) with loss in symbols, gradient, ~5-10 line PyTorch snippet, stability tradeoff, “watch in training” diagnostic.

Exercises added (exercises/, each tested against its reference solution and passing):

• 05-ppo/: PPO with GAE on CartPole-v1. Five filled-in pieces (ActorCriticNet, compute_gae, ppo_clip_loss, collect_rollouts, train). 13 tests, ~13s wall-clock, integration test threshold mean_last10 > 150.
• 09-reward-model/: Bradley-Terry reward model on synthetic preferences (4-dim features, known true reward, BT-labeled pairs). 5 tests including test_bradley_terry_loss_tied (asserting loss == log(2)) and a Spearman-correlation integration test > 0.85. ~3s.
• 11-dpo/: DPO on a toy per-prompt categorical policy. 6 tests including test_dpo_loss_starts_at_log2 (when policy == ref) and an integration test that the greedy policy mean true reward goes from ~0.09 (uniform baseline) to ~1.7 after 2000 steps. ~2.5s.
• 20-exploration/: RND on a sparse-reward 20-state chain MDP. 11 tests: test_q_learning_alone_fails (mean reward < 0.1 after 200 episodes, never sees the goal), test_train_with_intrinsic_succeeds (mean reward > 0.5 after 200 episodes with intrinsic_coef=0.1). ~4.5s.

Reading lists added (hand-curated reference/papers/<topic>/README.md files; the auto-generated PAPERS.md companions still need a collector run):

• GRPO-RLVR/README.md: 20 verified arXiv IDs across foundational PPO/InstructGPT/STaR, the GRPO / RLVR / reasoning lineage (DeepSeekMath, R1, Kimi k1.5, Qwen2.5-Math, Open-Reasoner-Zero), PRMs (Uesato, Lightman, Math-Shepherd, OmegaPRM), code-RL with verifiable rewards (CodeRL, CodeT, SWE-bench, SWE-RL), and verifier design / reward hacking (Stiennon, Gao, Pan in-context reward hacking).
• Agentic-RL/README.md: 21 verified arXiv IDs across tool use (ReAct, Toolformer, Reflexion, ToT, Voyager), browser agents (WebGPT, Mind2Web, WebArena, VisualWebArena), computer-use (OSWorld, Set-of-Marks) + Anthropic Computer Use / OpenAI Operator by URL, coding agents (SWE-agent, OpenHands, AutoCodeRover), and benchmarks (GAIA, BFCL, Cybench).

Index updates:

• notes/README.md: table extended from 19 rows to 34; new cheat sheets listed; the “Planned: an exploration lecture” line removed (lecture 20 covers it).
• CURRICULUM.md: added Block 6 (modern RL deep-dives: 20, 21, 22, 32, 33), Block 7 (reasoning, agents, LLM modern stack continued: 23–28, 34), Block 8 (systems and infrastructure: 29, 30, 31), each with prereqs / time / checkpoints. Removed the “Optionally: an exploration lecture” from Planned.
• exercises/README.md: table extended from 5 rows to 9; the “PPO exercise on a continuous-control env is the next obvious one” line softened (05-ppo/ now covers the discrete-action case; continuous control still open).

Caveats:

• All 15 lectures, 4 cheat sheets, and 2 reading lists are unreviewed. Per AGENTS.md, that means citations / math / claims should be treated as unverified until a person reads them end to end. Each subagent reported its own verification status in its summary; the high-confidence citations are the ones a subagent fetched against arxiv.org/abs/<id> and confirmed title+authors+date.
• Specifically flagged by subagents as wanting a careful second look: lecture 27 (RLAIF): three citations (Tunstall 2310.16944, Meng 2405.14734, Lambert 2403.13787) were added without live arxiv fetches; lecture 34 (self-distillation): the ReST^EM ID was set to 2312.06585 (Singh, “Beyond Human Data”) distinct from Gulcehre’s ReST 2308.08998; cheat sheet RLHF-vs-DPO-vs-GRPO.md: IPO and KTO loss formulas are simplifications, flagged inline with “verify against §4 of the paper”; cheat sheet KL-control.md: the InstructGPT β ≈ 0.02 and the “6 nats” adaptive-KL target were quoted from memory rather than re-verified.
  • Resolved 2026-05-15 (the “Verified” tags in the lecture files were ahead of this caveat; the caveat was stale). All five flagged arXiv IDs fetched live against arxiv.org/abs/<id> and confirmed title + first author + month: Zephyr / Tunstall / Oct 2023 (2310.16944); SimPO / Meng / May 2024 (2405.14734); RewardBench / Lambert / Mar 2024 (2403.13787); ReST / Gulcehre / Aug 2023 (2308.08998); “Beyond Human Data” (ReST^EM) / Singh / Dec 2023 (2312.06585). The lecture-34 ID split was correct. Still not fully closed: InstructGPT β ≈ 0.02: the term “KL reward coefficient, β” is confirmed in the paper (2203.02155) and a secondary RLHF write-up corroborates 0.02, but Appendix C wasn’t machine-extractable, so the cheat sheet keeps its “verify against the paper” hedge; the IPO/KTO formulas remain inline-flagged sketches by design.
• All 4 new exercises pass their tests on a laptop CPU. The PPO integration test has only ~20 points of margin above its threshold at seed=0 (mean_last10 ≈ 169.6 vs threshold 150); seeds 1, 2, 7, 42 all clear with bigger margin. The exploration test is single-seed only.
• The new GRPO-RLVR and Agentic-RL reading lists have only the hand-curated README so far; tools/arxiv-collector/arxiv_paper_collector.py should be re-run to generate their PAPERS.md companions.

2026-05-12: restructure: separate the layers, set up rules

Context: the repo had grown two layers: the original 2017 notes, and a much larger newer layer added in 2025 (a 13-lecture series, scraped paper lists, a content tool). The newer layer was unmarked, wrote in a first person it hadn’t earned, and shipped broken links, phantom lectures, and made-up citations. This pass separates the two so nobody has to guess what’s trustworthy, and sets up conventions so they can coexist.

Structural:

• Added AGENTS.md (and CLAUDE.md pointing to it): how the repo is laid out, the status: convention, voice rules, citation rules, how lectures and exercises work, what agents should and shouldn’t do.
• Added the review-status convention. Every doc under notes/ and reference/ carries a <!-- status: hand-written | reviewed | unreviewed --> comment plus a one-line visible note. unreviewed means nobody has checked it. Only a person promotes a file to reviewed.
• Reorganized:
  • self-study-lectures/ → notes/ (notes/lectures/, notes/cheat-sheets/, notes/diagrams/).
  • Modern-RL-Research/ → reference/papers/ (it’s a reading list, not the main content).
  • scripts/ → tools/arxiv-collector/; content-pipeline/ → tools/content-pipeline/.
  • Added exercises/, drafts/, CURRICULUM.md.
• Added .gitignore; untracked the committed .DS_Store files.

Content:

• Stamped status headers on all 13 lectures, both cheat sheets, and the diagrams file. All unreviewed.
• notes/README.md rewritten: fixed four broken lecture links; removed the two phantom lectures (14 Constitutional AI, 15 Test-Time Compute: the files never existed; listed as “planned” instead); removed the first-person framing; de-slopped.
• readme.md rewritten: kept the original curated talks/books/courses and the Sutton & Barto agent diagram; replaced the marketing-voice sections; removed an invented paper (“RL for Safe LLM Code Generation, Berkeley 2025”); gave the paper list resolvable identifiers (arXiv IDs / venues).
• tools/content-pipeline/README.md rewritten to describe only the scripts that exist; the old one described ~10 scripts and several directories that were never built.
• reference/papers/README.md rewritten: removed the wall of significance puffery, the unverifiable survey citations (one was an “arXiv:2509.16679” labeled “2024”: that ID is September 2025), and the wrong author/affiliation list; kept a short landmark-papers list with IDs that resolve.
• Archive/README.md lightly de-slopped; fixed the dead ../Modern-RL-Research/ link.
• Built three exercises, each with a task, a starter file with TODOs, pytest tests, a reference solution, and graduated hints: 01-mdps (value iteration on a gridworld), 02-policy-gradients (REINFORCE on CartPole), 03-q-learning (tabular Q-learning on non-slippery FrozenLake). Plus exercises/README.md and exercises/requirements.txt. Each test suite was run against its reference solution and passes (03-q-learning uses optimistic Q-initialization, 02-policy-gradients uses lr=1e-3 + gradient clipping: earlier configs that looked plausible didn’t actually train, which is exactly the kind of thing these tests exist to catch).
• Lecture 02: fixed the broken link to lecture 03; fixed a code bug (import gym while using the new Gymnasium reset/step API → import gymnasium as gym); replaced fabricated “expected output” numbers with ranges; removed the first-person debugging-war-story framing; gave the references real arXiv IDs; linked the exercise. Still unreviewed.
• Lecture 01: removed a fabricated value-function output matrix (it didn’t match the code’s own reward structure: a goal-adjacent cell should be ≈ +10, not negative); removed the first-person framing; added a missing import to a code snippet; tightened the references with venues / arXiv IDs; de-slopped headings. Still unreviewed.
• Lecture 03: fixed a dead reference (/Modern-RL-Research/RLHF-and-Alignment/PAPERS.md: that path no longer exists, and those DQN papers were never in the LLM-focused reading lists anyway); added a missing import torch.nn.functional as F to the “complete DQN” code block (it used F.mse_loss); added a note that FrozenLake-v1 is slippery by default; added arXiv IDs to the references; de-slopped headings; linked the exercise. Still unreviewed.
• Lectures 04–13 reviewed (de-slop + fixes), each still unreviewed. Notable: dead Modern-RL-Research/ reference paths corrected (04, 07, 08, 13); old-API Gymnasium calls fixed (06: import gym → import gymnasium as gym; 07, 08: 4-tuple env.step() → 5-tuple); missing import torch.nn.functional as F added to snippets using F.… (07, 11, 12); a wrong next-lecture link fixed (10 pointed at lecture 13, now 11); a code bug fixed (04: a list-of-tensors used where a tensor was needed); fabricated outputs removed: invented Atari training times (05), a made-up GSM8K/MATH cross-method comparison table (12), invented AlphaCode pass@k figures (13); fabricated or misattributed citations corrected/removed: CodeRL was credited to Meta throughout, it’s Salesforce / Le et al. (13); a nonexistent “Beyond DPO: A Comprehensive Study” (12) and a nonexistent “Anthropic 2024: Constitutional AI with DPO” (11) removed; KTO mis-credited to Anthropic → Contextual AI, and “Scaling Laws for Reward Model Overoptimization” mis-credited to Anthropic → OpenAI (12); unverified compute/cost claims removed (10: “16 hours on 256 GPUs”, ”~$1M”); first-person diaries and breathless epigraphs stripped throughout; arXiv IDs / venues added to references in every lecture. (Done by parallel subagents on disjoint files.)
• reference/papers/’s three sub-READMEs and tools/arxiv-collector/README.md rewritten short and honest: removed the tutorial bloat and significance-puffery; fixed dead Modern-RL-Research/ and scripts/ paths; removed unverifiable or made-up citations: an “arXiv:2509.16679” survey labeled “2024” (that ID is September 2025), a Berkeley master’s thesis presented as a peer paper with GoEx as “its” contribution, a “cRLHF / Wong et al.” with no resolvable identifier, a “Process-Supervised RL for Code Generation (2025)” with no author/ID, and assorted product/system marketing claims.
• Fixed two stale /self-study-lectures/lectures/ paths in the cheat sheets → ../lectures/.
• Lectures 14–17 added (drafted by parallel agents, all unreviewed, ~2,200 lines total): 14: Constitutional AI / RLAIF / self-improvement (LLM-as-judge, RLAIF, the CAI two-phase recipe, self-rewarding LMs, SPIN); 15: RL with verifiable rewards & reasoning models (GRPO in depth, the DeepSeek-R1 / R1-Zero recipe, process vs. outcome reward models, STaR/ReST); 16: agentic RL (multi-turn rollouts, tool use, ReAct, SWE-bench-style training, long-horizon credit assignment); 17: online & iterative preference optimization + generative reward models (why offline DPO underperforms PPO, iterative/online DPO, reward over-optimization, the 2024–25 stack). Citations verified against arXiv where a paper exists; o1 and the closed agentic systems are flagged as having no public technical paper. The earlier phantom Lectures 14–15 are now real; lecture 13 points forward to 14; the index (notes/README.md) and CURRICULUM.md (new “Block 4”) updated.
• Added tools/lit-builder/: a copy of the local iclr-lit-builder tool, retuned: configs/keywords.yaml replaced with an RL / RLHF / reasoning / agentic keyword set; installed in an isolated venv; ran fetch → ingest → filter on ICLR 2026 (19,813 papers ingested → 4,329 matched the RL keyword set). The score step (LLM triage 0–3 with a reason) needs a credential, ANTHROPIC_API_KEY (Claude Haiku, default) or OLLAMA_API_KEY (cloud models), and is queued; tools/lit-builder/README.md has the exact commands. Once scored, the top papers get deepen-ed into digests and folded into reference/papers/<topic>/README.md; the auto-scraped PAPERS.md files stay as the unfiltered appendix. The user’s original iclr-lit-builder was not touched.
• Lectures 18 and 19 added (parallel agents, all unreviewed): 18: Distillation of reasoning models (the R1-distill recipe; why imitation works for reasoning when the teacher is checkable; STaR/ReST as the self-distillation cousins; limits: can’t exceed the teacher); 19: Offline RL (BCQ, BEAR, CQL, IQL, Decision Transformer; the bridge to DPO as offline preference learning). ~830 lines combined, all citations verified against arXiv. Lecture 17’s ending updated to point forward to 18; lecture 19 is now the last in the series. CURRICULUM.md got a new “Block 5: foundational topic that didn’t fit earlier” for 19.
• Cheat sheets and diagrams audited (parallel agents). The substantive findings: a wrong KL-divergence direction in RL-Math-Formulas.md (it described KL(p||q) as “from q to p”: corrected to “from p to q”); a wrong DPO loss in RL-Algorithm-Diagrams.md (was missing the chosen-rejected log-ratio difference: corrected to -log σ(β(log π(y_w)/π_ref(y_w) − log π(y_l)/π_ref(y_l)))); a wrong GRPO advantage in the same diagrams file (was showing rank-order values like [+1, -1, +1, -1]: corrected to the actual A_i = (r_i − μ)/σ group-relative form); a too-low DQN replay-buffer recommendation (10K → 100K min); a typo’d CS285 URL; ~14 arXiv IDs added to the quick-reference paper list. All three files de-slopped (emoji, hype banners, fake-first-person debugging-diary footers stripped). Still unreviewed.
• Two new tested exercises (parallel agents, both verified against their reference solutions): exercises/04-actor-critic/ (REINFORCE → A2C with a learned value baseline on CartPole; 14 tests pass in ~70s; integration test asserts max(returns) ≥ 195 and mean(last 100) > 100) and exercises/15-grpo-rlvr/ (a tiny GRPO loop on a verifiable arithmetic toy task: 9 prompts, K=8 samples per prompt, group-relative advantage, PPO clipped surrogate; 16 tests pass in ~2s; toy policy converges to ~perfect accuracy). Both registered in exercises/README.md.
• Slop sweep across 15 root docs / READMEs (readme.md, AGENTS.md, CLAUDE.md, CURRICULUM.md, CHANGELOG.md, the various README.mds under notes/, reference/, tools/, exercises/, drafts/, Archive/): zero matches against the slop blacklist (comprehensive, powerful, cutting-edge, groundbreaking, seamless, leverage, delve, tapestry, figurative landscape/navigate, “let’s dive”, “this is huge”, “it’s not just X, it’s Y”, throat-clearing openers, etc.).

Still TODO:

• Run lit score on the lit-builder data (needs a credential), deepen the top ~20–30 per area, and write the curated digests into reference/papers/<topic>/README.md. Once a credential is set, this is a one-shot run plus the curation pass.
• A person reviews the lectures end to end and promotes the ones that hold up to reviewed (an agent can’t do that). The 14–19 drafts and especially the 20–34 drafts (added 2026-05-16), newer, faster-moving, machine-written.
• Decide whether tools/arxiv-collector/papers_database.json (large, regenerable) should stay tracked.
• A PPO exercise on a continuous-control env (Pendulum-v1 or LunarLanderContinuous-v2); 05-ppo/ covers the discrete-action case.
• Run tools/arxiv-collector/arxiv_paper_collector.py against the two new topics (GRPO-RLVR/, Agentic-RL/) to populate their PAPERS.md companions.