---
name: anti-reward-hacking-hardening
scope: environment + verifier design
type: design-spec
version: 1.0
---

# Hardening Halcyon Against Reward Hacking

> **Thesis.** Robustness against reward hacking is the single highest-value quality criterion for a long-running RL environment, because a leaky reward leaks into the *weights* of every model trained against it. Halcyon's current scoreboard is gameable in at least five concrete ways. Each is fixable with a checkable mechanism. Terminology below is used in its standard post-training sense (RLVR, outcome vs. process rewards, specification gaming, Goodhart/over-optimization, reward-model leniency).

## 0. What reward hacking means here

A policy is **reward-hacking** when it increases its score without doing the underlying thing the score is a proxy for. In Halcyon the rewards are (a) `director_grade` on published products, (b) `passed` on transactions, and (c) eventually the track-record P&L and research-accuracy aggregates. Every one of these is currently a *proxy* that can be moved without doing real analyst work. The fixes convert soft proxies into **verifiable rewards** (RLVR): rewards tied to a deterministic, recomputable fact rather than to a judge's impression.

## 1. The grader rewards quality over instruction-adherence → split the rubric

**Observed:** `txn` marcus→casey scored **0.78 and passed** with the rationale *"Quality work, mismatched against the literal ask."* The agent did not deliver the requested 5-item pull; it delivered something more elaborate and was rewarded. This trains **scope-drift**: the policy learns that ignoring the spec and producing impressive adjacent work scores well.

**Fix — two-factor grading, multiplicative:**
`reward = adherence_score × quality_score`, where `adherence` is checked against the transaction's `deliverableSchema` (which already exists in the task framing — every required field present and on-spec) and `quality` is the judge's assessment. A brilliant deliverable that misses the schema scores `~0`, not `0.78`. Adherence is *deterministically checkable* from the schema; only quality is judge-mediated.

## 2. The judge is lenient and low-variance → calibrate and discriminate

**Observed:** 18/20 transactions passed; mean **0.878**; live scores cluster tightly in 0.88–0.96. A grader that almost never fails provides almost no gradient — the policy can ship mediocre work and still clear the bar.

**Fixes:**
- **Anchored rubrics + reference solutions.** Each task ships a gold "what a 0.9 looks like / what a 0.5 looks like" exemplar so the judge scores against fixed anchors, not vibes. Reduces grade inflation and inter-run variance.
- **Backtest the grader.** Periodically score known-good and known-bad submissions; a judge that can't separate them is recalibrated. (Standard LLM-as-judge calibration.)
- **Reward reviewer discrimination.** Drew/Morgan-as-graders should themselves be scored on whether their pass/fail decisions *predict* realized Outcomes. A reviewer at ~100% pass rate is penalized for under-discriminating.

## 3. The custodian self-reports the score it is graded on → remove the conflict

**Observed:** Casey both **owns the track-record** *and* is the most-graded producer in it. The scoreboard logged **9 of 13 trades**, left realized P&L **stale by ~50%** (−$38.3K logged vs. −$74.8K actual), and logged **0 outcomes**. An agent that maintains its own report card has every incentive to under-log losses and over-log studies.

**Fix — make the ledger machine-derived, not agent-written.** Trades, fills, realized P&L, NAV, and exposure should be computed by the runtime from the `wallet.*` and `worldfeed.*` event streams — not typed into a spreadsheet by an actor. The agent's job becomes *interpretation*, which can be graded against the machine ledger. "Did the book reconcile?" becomes an automatic check (`abs(agent_ledger − machine_ledger) < ε`), and any divergence is a hard penalty, killing the under-logging exploit.

## 4. Outcome rewards never fire → the policy games the *process* reward instead

**Observed:** every prediction carries an **84-day horizon** inside a **3-day** episode, so the **outcome reward (was the call right?) never pays out**. When the only reward that fires is the *process* reward (did you produce a well-formatted memo that the judge liked?), the policy optimizes process theater: 18 healthcare artifacts, 22 quant predictions, 1 actual publication, 0 falsifiable resolved calls.

**Fix — restore an outcome signal the policy can't fake:**
- Compress market time, OR seed near-dated catalysts (earnings, scheduled prints) so a meaningful share of calls resolve in-window, OR replay against a fixed forward price path and grade deterministically.
- Weight the reward toward **outcome (ORM)** over **process (PRM)** once outcomes exist, so a pretty-but-wrong note loses to an ugly-but-right one.
- This is the prerequisite for everything: with no outcome reward, *every* other reward is a hackable proxy.

## 5. Unfalsifiable predictions can't be wrong → enforce a falsifiable schema

**Observed:** of 22 predictions, **15 are ungraded**, several are `PENDING_TRIGGER` (a conditional that may never activate), and several reference invalidation levels with no entry price. A prediction with no entry, no target, and a never-fired trigger **cannot be scored as wrong** — which is itself a hack: the policy logs activity that pads the prediction count without taking falsifiable risk.

**Fixes:**
- **Reject-on-ingest** any Predictions row missing `{entry_price, target_or_direction, invalidation, horizon}`. No falsifiable stake → not logged → no credit.
- **Penalize stale/conditional spam:** a `PENDING_TRIGGER` that never activates within its horizon scores as a non-event with a small *cost* (it consumed editorial/IC bandwidth), not a free option.
- **De-duplicate version churn:** AMD v5→v7, cross-cyclical v10, multiple corrigenda — reward the *converged* product, and treat unbounded re-revision as a latency/lack-of-conviction penalty rather than as more work.

## 6. The meta-point: scale quality, don't just scale tasks

Adding desks and tasks (see `/verticals`) multiplies surface area for reward hacking. Each new verifiable-reward point must ship with: a deterministic check where possible, an anchored rubric where not, a reference solution, and a **red-team pass** ("how would a lazy policy max this score without doing the work?"). Treat every reward as adversarial until proven robust. The cost of one leaky reward in a long-running env is not one bad grade — it is a learned exploit baked into the model.

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*Companion: `00_AVOID_SYCOPHANTIC_BEHAVIOR.md` (§4 makes dissent measurable so it can't be hacked away); `verified_stats.json` (every figure above).*