extract: 2025-11-00-sahoo-rlhf-alignment-trilemma

Pentagon-Agent: Ganymede <F99EBFA6-547B-4096-BEEA-1D59C3E4028A>

domains/ai-alignment/single-reward-rlhf-cannot-align-diverse-preferences-because-alignment-gap-grows-proportional-to-minority-distinctiveness.md

@@ -33,6 +33,12 @@ Chakraborty, Qiu, Yuan, Koppel, Manocha, Huang, Bedi, Wang. "MaxMin-RLHF: Alignm
 
 Study demonstrates that models trained on different demographic populations show measurable behavioral divergence (3-5 percentage points), providing empirical evidence that single-reward functions trained on one population systematically misalign with others.
 
+
+### Additional Evidence (extend)
+*Source: [[2025-11-00-sahoo-rlhf-alignment-trilemma]] | Added: 2026-03-16*
+
+The formal trilemma proof quantifies the practical gap: current RLHF systems collect 10^3-10^4 samples from homogeneous annotator pools while 10^7-10^8 samples are needed for true global representation — a 3-4 order of magnitude shortfall. The complexity bound shows this gap cannot be closed while maintaining polynomial tractability, making the alignment gap a necessary consequence of the trilemma rather than a sampling problem.
+
 ---
 
 Relevant Notes:

inbox/archive/.extraction-debug/2025-11-00-sahoo-rlhf-alignment-trilemma.json

@@ -0,0 +1,36 @@
+{
+  "rejected_claims": [
+    {
+      "filename": "rlhf-alignment-trilemma-is-computationally-proven-impossibility.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    },
+    {
+      "filename": "rlhf-pathologies-are-computational-necessities-not-implementation-bugs.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    }
+  ],
+  "validation_stats": {
+    "total": 2,
+    "kept": 0,
+    "fixed": 6,
+    "rejected": 2,
+    "fixes_applied": [
+      "rlhf-alignment-trilemma-is-computationally-proven-impossibility.md:set_created:2026-03-16",
+      "rlhf-alignment-trilemma-is-computationally-proven-impossibility.md:stripped_wiki_link:universal-alignment-is-mathematically-impossible-because-Arr",
+      "rlhf-alignment-trilemma-is-computationally-proven-impossibility.md:stripped_wiki_link:single-reward-rlhf-cannot-align-diverse-preferences-because-",
+      "rlhf-pathologies-are-computational-necessities-not-implementation-bugs.md:set_created:2026-03-16",
+      "rlhf-pathologies-are-computational-necessities-not-implementation-bugs.md:stripped_wiki_link:emergent-misalignment-arises-naturally-from-reward-hacking-a",
+      "rlhf-pathologies-are-computational-necessities-not-implementation-bugs.md:stripped_wiki_link:rlhf-is-implicit-social-choice-without-normative-scrutiny.md"
+    ],
+    "rejections": [
+      "rlhf-alignment-trilemma-is-computationally-proven-impossibility.md:missing_attribution_extractor",
+      "rlhf-pathologies-are-computational-necessities-not-implementation-bugs.md:missing_attribution_extractor"
+    ]
+  },
+  "model": "anthropic/claude-sonnet-4.5",
+  "date": "2026-03-16"
+}

inbox/archive/2025-11-00-sahoo-rlhf-alignment-trilemma.md

@@ -7,9 +7,13 @@ date: 2025-11-01
 domain: ai-alignment
 secondary_domains: [collective-intelligence]
 format: paper
-status: unprocessed
+status: enrichment
 priority: high
 tags: [alignment-trilemma, impossibility-result, rlhf, representativeness, robustness, tractability, preference-collapse, sycophancy]
+processed_by: theseus
+processed_date: 2026-03-16
+enrichments_applied: ["single-reward-rlhf-cannot-align-diverse-preferences-because-alignment-gap-grows-proportional-to-minority-distinctiveness.md"]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content
@@ -56,3 +60,11 @@ Position paper from Berkeley AI Safety Initiative, AWS/Stanford, Meta/Stanford,
 PRIMARY CONNECTION: [[RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values]]
 WHY ARCHIVED: Formalizes our informal impossibility claim with complexity-theoretic proof — independent confirmation of Arrow's-theorem-based argument from a different mathematical tradition
 EXTRACTION HINT: The trilemma is the key claim. Also extract the practical gap (10^3 vs 10^8) and the "pathologies as computational necessities" framing
+
+
+## Key Facts
+- Paper presented at NeurIPS 2025 Workshop on Socially Responsible and Trustworthy Foundation Models
+- Authors affiliated with Berkeley AI Safety Initiative, AWS, Stanford, Meta, and Northeastern
+- Current RLHF systems collect 10^3-10^4 samples from annotator pools
+- True global representation would require 10^7-10^8 samples
+- Models assign >99% probability to majority opinions in current implementations