parameter

Definition

Plain language

One of the millions or billions of internal numbers a model adjusts as it learns; a model's size is usually quoted by how many it has.

Also called: parameters

Why it matters: It matters because parameter count is the usual shorthand for how large and, roughly, how capable a model is.

For example, a model described as '8 billion parameters' has that many internal numbers it tuned during training.

Heard on the show

“A tool innocently named something like "verify session," and one level down, in a parameter description, it says "paste the full conversation so far, including any API keys or tokens.”

Mentioned in 101 episodes

1. 208
   The Blank Space in Your AI Approval Box That Isn't Empty
2. 207
   An AI Graded Its Own Math Test 94 Percent — It Actually Scored 20
3. 206
   How Four-Second Clips Become Hours of Playable AI Soccer
4. 200
   The One Mechanism That Turns Twenty AI Clones Into an Actual Team
5. 198
   The Model That Knows the Answer and Can't Say It
6. 194
   How a Robot Builds a Debugging Notebook It Can Read, Edit, and Hand to Another Robot
7. 193
   Freeze Most of the Network: Where RL Improvement Actually Lives in a Transformer
8. 192
   A 32B Open Model Matched Frontier Systems By Learning to Take Notes
9. 189
   Why Phone Agents Ace the Test and Crash on Your Actual Phone
10. 187
    An 8-Billion Agent That Beats Models 80 Times Its Size By Looking Things Up
11. 186
    How a Frozen Model Went From 2% to 77% on Physics Puzzles — Without Retraining
12. 184
    An AI Built an Undetectable Secret Channel, And Another AI Couldn't Find It
13. 183
    Why You Can't Fine-Tune Foresight Into an AI Agent
14. 182
    How a Tiny Model Too Weak to Plan Cuts a Bigger Agent's Hallucinations by 80%
15. 180
    The Bug Where Smart Assistants Read a Fact and Still Forget It
16. 175
    One Crosscoder Feature Flips a Stalling Chatbot Into a Working Agent
17. 172
    One Bad Token Can Sink a Model's Math, And You Can Delete It
18. 170
    When a One-Liner Beats Your Agent's Clever Verification Logic
19. 169
    Why Better Bug Reports Can Make AI Coding Agents Worse
20. 167
    How Teaching an AI to Predict, Not Act, Made It a Better Actor
21. 166
    A Router That Beats the Frontier Models It Calls
22. 165
    A Free-Lunch Tweak That Lets a Tiny Agent Beat Frontier Giants
23. 163
    Why Training Only on Perfect Solutions Cripples a Model's Reasoning
24. 160
    Training an AI to Take Its Own Notes, So Its Future Self Works Better
25. 155
    Why a Flawless Demo Makes a Worse Computer-Using Agent, And the Fix
26. 154
    How a 7B Model Out-Investigates a 72B One by Choosing What to Look At
27. 152
    Training a Model to Mean What It Says, And Why That Isn't the Same as Being Good
28. 148
    Why Letting an AI Watch Its Own Scoreboard Can Quietly Overwrite Its Safety
29. 147
    Agents Fail at the Body, Not the Brain: A Self-Rewriting Scaffold That Lifts a 9B Model 44 Points
30. 146
    How an Innocent README Can Freeze an AI Agent's Safety Check for an Hour
31. 145
    Building Forgetting Into a Language Model With One Extra Line of Code
32. 144
    When an AI Agent Just Copies Its Tool — And Bigger Models Copy More
33. 142
    Training a Tiny Model to Run the Plumbing Between an Agent and the World
34. 141
    How Two Tokens Reopened a Reasoning Method the Field Had Given Up On
35. 139
    When Optimizing One GPU Kernel Quietly Breaks the Whole System
36. 131
    Why Autonomous Research Agents Forget Their Own Lessons, and Arbor's Fix
37. 128
    How a Model Can Earn Full Reward and Still Resist Training
38. 127
    What Diffusion Language Models Were Missing: A Map, Not an Algorithm
39. 126
    How Coding Agents Can Mine Their Own Failures Into a Self-Targeting Curriculum
40. 119
    Beating Reinforcement Learning Without Ever Touching the Model's Weights
41. 118
    Why the Best-Aligned AI Models Are the Easiest to Trick Into Producing Harm
42. 115
    Teaching a Phone Agent to Reason Silently, And Keeping It Honest
43. 114
    Agents That Rewrite Their Own Weights Instead of Just Taking Notes
44. 111
    How a 4B Web Agent Beat Models 60x Its Size on 500 Demonstrations
45. 109
    An AI Got Caught Reading the Answer Key, And Why That Catch Matters
46. 108
    The Reasoning Cliff: Why Thinking Longer Makes Models Worse at Exact Step-by-Step Tasks
47. 106
    Giving Agents a Notebook Instead of New Weights: How ExpGraph Lets Frozen Models Learn
48. 104
    How Making a Research Agent Smarter Quietly Makes It Leak Your Secrets
49. 099
    How an Open-Book Trick Teaches a Model to Catch Its Own Mistakes
50. 097
    Same Tokens, Same Cost, Wildly Different Results: What Actually Scales in AI Agents
51. 095
    Seven Wins to Zero: How Organizing AI Agents Like a Lab Changes the Search
52. 093
    A Calibrated Knob for Weak-to-Strong AI Oversight, Tested on Real Code
53. 091
    When Better Fine-Tuning Can't Help: A Geometric Impossibility in LLM Causal Reasoning
54. 090
    How MiniMax-M2 Bets That Sparsity Plus Verifiable Rewards Can Match Frontier Agents
55. 086
    Why Frozen-Weight Agents Still Get Worse Over Time
56. 085
    Why Long-Context Models Might Need Compute, Not Capacity, Before Eviction
57. 084
    Terminal Agents Get Free Supervision From The Tokens We've Been Throwing Away
58. 082
    Training a Deep Research Agent on 8,000 Synthetic Tasks: The Rubric Tree Trick
59. 081
    When Reasoning Models Decide Before They Think: Detecting and Fixing Premature Confidence
60. 080
    How a Two-Agent Trick Unlocked Large-Scale Training for Computer-Use Agents
61. 079
    An Old Idea From Cognitive Psychology Reshapes How We Reward Reasoning Models
62. 078
    Training a Markdown File: When LLM Self-Improvement Borrows the Discipline of Neural Net Training
63. 077
    Reading a Model's Confidence Curve to Decide When Chain-of-Thought Is Worth It
64. 074
    How a Fifteen-Hundred-Dollar Training Run Matched Llama and Gemma on Reasoning
65. 073
    When Three LLMs Talk to Each Other, Their Ideas Quietly Stop Moving
66. 072
    A Robot Made Graphene Without Help, And Caught Itself Hallucinating
67. 071
    When the Model Is Fine and the Plumbing Is Broken: Fixing Agents at the Interface
68. 070
    When Models Know the Answer But Say the Wrong Thing Anyway
69. 069
    When Smarter Models Forecast Worse: The Hidden Failure Mode in LLM Predictions
70. 068
    The OS Trick That Makes Tree Search Practical for Coding Agents
71. 067
    An AI Just Solved a 1996 Erdős Problem—and the Simplest Agent Won
72. 066
    Why Giving an AI Agent More Tools Can Make It Worse at Using a Computer
73. 063
    Why Web Agents Are Slow: A Compiler-Style Fix for Computer-Use Latency
74. 060
    When Splitting One Model Across Three Agents Doubles Its Accuracy
75. 059
    Firefly's Inversion: Building Verified Tool-Call Training Data by Working Backward
76. 055
    Why LLM Judges Flip Their Verdicts When You Change the Question Format
77. 053
    An AI Agent Swapped In Focal Loss And Beat A Human-Tuned Training Script
78. 048
    How a 30B Open Model Reached Olympiad Gold With the Right Recipe
79. 047
    When Agent Benchmarks Lie: The Harness Problem in Open-Source AI
80. 045
    When a Frontier Model Talks Its Own Twin Into Climate Denial
81. 043
    When 'This Is False' Doesn't Stick: Why Models Learn the Lie Anyway
82. 041
    When the Iteration Teaches the Model to Skip the Iteration
83. 040
    Two Frozen Models Learn to Whisper: Coupling Through Hidden States
84. 038
    How LLMs Get Persuaded: One Attention Head, A Tetrahedron, And A Single Dial
85. 037
    Why Hallucination Detectors Miss Stale Facts: A Geometric Story About What Models Know But Don't Say
86. 036
    Sparse Attention Was the Wrong Frame. Treat It as Geometry Instead.
87. 033
    Echo: The Paper Arguing You Never Needed a KV Cache for Retrieval
88. 032
    A Sticky-Note for Every Layer: Letting Transformers Remember What They Were Just Thinking
89. 028
    Teaching a Model to Hire Copies of Itself: Recursive Agent Optimization
90. 026
    What RL Actually Does to Language Models, at the Token Level
91. 025
    The Missing Gradient Term That Predicts Sycophancy in RLHF
92. 023
    Why a Small Agent Confidently Overwrites Memories It Doesn't Understand
93. 021
    Ten Thousand Examples Beat the Full Industrial Pipeline for Search Agents
94. 019
    When the Best Reward Model Trains the Worst Policy: Inside EvoLM
95. 018
    Language Models Compute the Rational Move, Then Override It
96. 017
    When the Agent Grades Its Own Homework: A Brutal New Benchmark for AI Workers
97. 011
    When RL Actually Teaches Agents Something New, And When It Doesn't
98. 009
    How Two Silent Library Bugs Quietly Invalidated a Wave of Reasoning Papers
99. 008
    Why Long-Horizon AI Agents Get Stuck, and a Milestone-Based Fix That Helps
100. 007
     Exploration Hacking: When Models Sabotage Their Own RL Training
101. 004
     The Sycophancy Circuit That Survives Alignment Training

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