---
type: analysis
title: "Pokemon TCG - Research Backlog"
description: "Explicitly deferred research directions for the Pokemon TCG agent, separated from implemented architecture."
tags: [pokemon-tcg, research-backlog, grpo, self-play, alpha-fita, population]
timestamp: "2026-08-15T17:24:00-03:00"
---

# Pokemon TCG - Research Backlog

## Boundary

This page lists research directions, not implemented capabilities. Alefita keeps
scientific decision authority. The page records option space and technical
dependencies so future work does not get confused with the current system.

## Current Implemented Base

The current architecture has recurrent MLX behavioral-cloning training,
would-KO metadata and auxiliary heads integrated into the Parquet-era primary
pipeline, deterministic action coverage, and strict-FP32 PyTorch submission
inference. The Prospective V2 sidecar and lateral planner are historical
design/runtime artifacts removed at `a942373`; their group-relative objective
is a research antecedent, not a current sidecar runtime. The FP16 submission
artifact belongs to the historical delivery record. See
[[pokemon_tcg_prospective_v2]], [[pokemon_tcg_training_overhaul_2026_07_29]]
and [[pokemon_tcg_current_state_reconciliation]].

That is not strict on-policy GRPO and not a population self-play system.

## Deferred Directions

| Direction | Role | Status |
|---|---|---|
| AlphaFita / FitaStar | Project-level names for stronger search/self-play research lines | Backlog |
| AlphaStar-like population self-play | Maintain a population of policies, opponents, exploiters, and evaluation snapshots | Backlog |
| Strict on-policy GRPO | Sample branches from the current policy, retain behavior log-probs, compute group-relative advantages on-policy | Backlog |
| Fita GRPO continuation | Extend the historical group-relative prospective supervision framing toward a stricter policy-gradient path | Backlog |
| Task embeddings amortized across tasks | Condition policy/planner on task descriptors so repeated subtasks share representation | Backlog |
| Learned task weighting | Learn or adapt loss/task weights instead of manually fixed coefficients | Backlog |
| Deck selection | Optimize a deck outer loop from local self-play and matchup evidence | Backlog |
| Kimi K3 / AoR / KDA ideas | External research concepts to evaluate on their own terms before importing | Backlog |
| Adapter libraries | Opponent/archetype/date-specific low-rank modules with explicit leakage controls | Backlog |
| Population or PSRO-style deck-policy ecology | Model non-transitive matchups across decks and policies | Backlog |

## Strict GRPO Difference

The historical Prospective V2 objective can use group-relative advantages across
counterfactual branches and may include behavior/reference log-prob terms when
available. However, a strict on-policy GRPO claim requires a stronger contract:

- branches are sampled from the current policy being updated;
- behavior log-probs are stored for the sampled actions;
- policy updates compare against those stored behavior probabilities;
- data collection and update cadence define an on-policy or near-on-policy
  window;
- reward normalization and clipping are tied to that sampled group.

The historical sidecar was offline and replay-derived. Its design can support
future GRPO research, but it is not present in the current runtime and was not
proof of strict on-policy GRPO.

## Research Hygiene

Backlog items should carry:

- date and source of the idea;
- evidence needed before implementation;
- illegal-information risks;
- expected artifact changes;
- tests that would distinguish real progress from data leakage or opponent-set
  overfitting.

## Related Pages

- [[pokemon_tcg_prospective_v2]]
- [[pokemon_tcg_would_ko_prospective_search]]
- [[pokemon_tcg_action_coverage]]
- [[pokemon_tcg_ladder_and_research]]
- [[pokemon_tcg_deck_strategy]]