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

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

domains/ai-alignment/rlhf-is-implicit-social-choice-without-normative-scrutiny.md

@@ -39,6 +39,12 @@ RLCF makes the social choice mechanism explicit through the bridging algorithm (
 
 Comprehensive February 2026 survey by An & Du documents that contemporary ML systems implement social choice mechanisms implicitly across RLHF, participatory budgeting, and liquid democracy applications, with 18 identified open problems spanning incentive guarantees and pluralistic preference aggregation.
 
+
+### Additional Evidence (extend)
+*Source: [[2025-11-00-sahoo-rlhf-alignment-trilemma]] | Added: 2026-03-16*
+
+The trilemma formalizes why RLHF's implicit social choice is problematic: achieving epsilon-representativeness (epsilon <= 0.01) and delta-robustness (delta <= 0.001) simultaneously requires super-polynomial compute, making the 'strategic relaxation' of representativeness a practical necessity that RLHF implementations make without explicit acknowledgment.
+
 ---
 
 Relevant Notes:

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

@@ -39,6 +39,12 @@ Study demonstrates that models trained on different demographic populations show
 
 An & Du's survey reveals the mechanism behind single-reward failure: RLHF is doing social choice (preference aggregation) but treating it as an engineering detail rather than a normative design choice, which means the aggregation function is chosen implicitly and without examination of which fairness criteria it satisfies.
 
+
+### Additional Evidence (extend)
+*Source: [[2025-11-00-sahoo-rlhf-alignment-trilemma]] | Added: 2026-03-16*
+
+The formal trilemma proof shows preference collapse is not just empirically observed but mathematically necessary: single-reward RLHF cannot capture multimodal preferences even in theory. The paper quantifies the practical gap: current systems use 10^3-10^4 samples from homogeneous pools while 10^7-10^8 samples are needed for global representation — a 3-4 order of magnitude shortfall.
+
 ---
 
 Relevant Notes:

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

@@ -0,0 +1,37 @@
+{
+  "rejected_claims": [
+    {
+      "filename": "rlhf-alignment-trilemma-proves-no-system-can-simultaneously-achieve-representativeness-tractability-and-robustness.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    },
+    {
+      "filename": "rlhf-pathologies-are-computational-necessities-not-implementation-bugs-because-preference-collapse-sycophancy-and-bias-amplification-follow-from-the-trilemma.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    }
+  ],
+  "validation_stats": {
+    "total": 2,
+    "kept": 0,
+    "fixed": 7,
+    "rejected": 2,
+    "fixes_applied": [
+      "rlhf-alignment-trilemma-proves-no-system-can-simultaneously-achieve-representativeness-tractability-and-robustness.md:set_created:2026-03-16",
+      "rlhf-alignment-trilemma-proves-no-system-can-simultaneously-achieve-representativeness-tractability-and-robustness.md:stripped_wiki_link:universal-alignment-is-mathematically-impossible-because-Arr",
+      "rlhf-alignment-trilemma-proves-no-system-can-simultaneously-achieve-representativeness-tractability-and-robustness.md:stripped_wiki_link:single-reward-rlhf-cannot-align-diverse-preferences-because-",
+      "rlhf-alignment-trilemma-proves-no-system-can-simultaneously-achieve-representativeness-tractability-and-robustness.md:stripped_wiki_link:rlhf-is-implicit-social-choice-without-normative-scrutiny.md",
+      "rlhf-pathologies-are-computational-necessities-not-implementation-bugs-because-preference-collapse-sycophancy-and-bias-amplification-follow-from-the-trilemma.md:set_created:2026-03-16",
+      "rlhf-pathologies-are-computational-necessities-not-implementation-bugs-because-preference-collapse-sycophancy-and-bias-amplification-follow-from-the-trilemma.md:stripped_wiki_link:single-reward-rlhf-cannot-align-diverse-preferences-because-",
+      "rlhf-pathologies-are-computational-necessities-not-implementation-bugs-because-preference-collapse-sycophancy-and-bias-amplification-follow-from-the-trilemma.md:stripped_wiki_link:emergent-misalignment-arises-naturally-from-reward-hacking-a"
+    ],
+    "rejections": [
+      "rlhf-alignment-trilemma-proves-no-system-can-simultaneously-achieve-representativeness-tractability-and-robustness.md:missing_attribution_extractor",
+      "rlhf-pathologies-are-computational-necessities-not-implementation-bugs-because-preference-collapse-sycophancy-and-bias-amplification-follow-from-the-trilemma.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", "rlhf-is-implicit-social-choice-without-normative-scrutiny.md"]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content
@@ -56,3 +60,12 @@ 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 documented cases
+- Three strategic relaxation pathways proposed: constrain representativeness to ~30 core values, scope robustness narrowly to plausible threats, or accept super-polynomial costs for high-stakes applications