MineRL Diamond Challenge
MineRL is a research project / competition series using Minecraft as a long-horizon RL environment. The flagship Diamond Challenge tasks an agent with obtaining a diamond from raw world — requires deep tech-tree planning across thousands of steps. DreamerV3 (DeepMind, 2023-12) famously became the first to solve it from scratch without human data.
At a glance
- Type
- Minecraft RL benchmark — obtain a diamond
- Tier
- T2
- Section
- Memory benchmarks & evaluation
- Created
- 2019 (Guss et al. IJCAI 2019)
- Latest release
- not applicable — not OSS
- License
- MIT
- GitHub
- ~700★ Python
- Pricing
- not applicable — open benchmark; free to use
- Funding
- not applicable — not commercial
Taxonomy
- storage
- n/a
- retrieval
- n/a
- persistence
- n/a
- update
- read-only
- unit
- n/a
- governance
- n/a
- conflict
- n/a
When to use
Optimised for: not applicable - eval dataset, not a system
Anti-fit: not applicable - eval dataset, not a system
Pros & cons
Pros
Real, complex, long-horizon environment; competition history with public leaderboards; visual modality forces realistic perception.
Cons
Minecraft proprietary licensing complicates redistribution; rewards extremely sparse (~24k steps to first diamond); legacy versions of MC mod required.
Claims & capabilities
NeurIPS 2019/2020/2021 competitions; DreamerV3 first to solve from scratch (2023-12); remains a canonical world-model benchmark
Technical surface
- API surface
- not applicable — eval dataset, not a system
- Backend storage
- not applicable — eval dataset, not a system
- Deployment
- not applicable — run locally
- Embedding model
- not applicable — eval dataset, not a system
- Multi-tenancy
- not applicable — eval dataset, not a system
- MCP
- not applicable — benchmark / evaluation harness
- A2A
- not applicable — benchmark / evaluation harness
- OpenTelemetry
- not applicable — benchmark / evaluation harness
Similar systems
Other memory benchmarks & evaluation in the catalog, ranked by inbound references.
- LoCoMo T3
Conversations spanning up to 35 sessions / 300 turns / ~9K tokens, built from LLM-agent personas grounded on temporal event graphs and verified by human annotators. Tests QA (single-hop, multi-hop, temporal, commonsense, adversarial), event-graph summarisation, multimodal dialogue.
- LongBench T3
Tsinghua THUDM. 21 datasets across 6 categories (single-doc QA, multi-doc QA, summarisation, few-shot ICL, synthetic, code completion) in English + Chinese. ~6.7K words English / 13.4K chars Chinese average. v2 at ACL 2025 with harder, longer instances.
- LongMemEval T3
5 core memory abilities — info extraction, multi-session reasoning, temporal reasoning, knowledge updates, abstention — via 500 curated questions in synthetic chat histories. Two scales: LongMemEval-S (115K tokens), LongMemEval-M (up to 1.5M).
- RULER T3
NVIDIA. Extends NIAH with multi-needle retrieval, multi-hop entity tracing, aggregation, QA — configurable for arbitrary length / difficulty. Evaluated 17 long-context models on 13 tasks.
- InfiniteBench (∞Bench) T3
Tsinghua / OpenBMB. First benchmark with average input >100K tokens. 12 tasks across English + Chinese: long novel QA, code debugging, math in long context, summarisation, retrieval. Realistic documents (novels, code) rather than synthetic filler.
- Atari 100k T3
Sample-efficiency benchmark protocol on Arcade Learning Environment (ALE) — agents limited to 100k environment steps (~2 hours of game-play) before evaluation. Introduced by SimPLe (Kaiser et al. 2019) and adopted by EfficientZero, IRIS, BBF, DreamerV3, Storm. Tests world-model / model-based methods where memory-as-imagination matters more than huge replay buffers.