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Enterprises must know if the fashions that energy their functions and brokers work in real-life situations. This sort of analysis can typically be complicated as a result of it’s onerous to foretell particular situations. A revamped model of the RewardBench benchmark appears to provide organizations a greater thought of a mannequin’s real-life efficiency.
The Allen Institute of AI (Ai2) launched RewardBench 2, an up to date model of its reward mannequin benchmark, RewardBench, which they declare offers a extra holistic view of mannequin efficiency and assesses how fashions align with an enterprise’s targets and requirements.
Ai2 constructed RewardBench with classification duties that measure correlations by inference-time compute and downstream coaching. RewardBench primarily offers with reward fashions (RM), which might act as judges and consider LLM outputs. RMs assign a rating or a “reward” that guides reinforcement studying with human suggestions (RHLF).
Nathan Lambert, a senior analysis scientist at Ai2, advised VentureBeat that the primary RewardBench labored as supposed when it was launched. Nonetheless, the mannequin setting quickly developed, and so ought to its benchmarks.
“As reward fashions grew to become extra superior and use instances extra nuanced, we rapidly acknowledged with the neighborhood that the primary model didn’t absolutely seize the complexity of real-world human preferences,” he stated.
Lambert added that with RewardBench 2, “we got down to enhance each the breadth and depth of analysis—incorporating extra various, difficult prompts and refining the methodology to replicate higher how people really choose AI outputs in apply.” He stated the second model makes use of unseen human prompts, has a more difficult scoring setup and new domains.
Utilizing evaluations for fashions that consider
Whereas reward fashions take a look at how properly fashions work, it’s additionally essential that RMs align with firm values; in any other case, the fine-tuning and reinforcement studying course of can reinforce unhealthy conduct, comparable to hallucinations, cut back generalization, and rating dangerous responses too excessive.
RewardBench 2 covers six totally different domains: factuality, exact instruction following, math, security, focus and ties.
“Enterprises ought to use RewardBench 2 in two other ways relying on their software. In the event that they’re performing RLHF themselves, they need to undertake the perfect practices and datasets from main fashions in their very own pipelines as a result of reward fashions want on-policy coaching recipes (i.e. reward fashions that mirror the mannequin they’re attempting to coach with RL). For inference time scaling or information filtering, RewardBench 2 has proven that they’ll choose the perfect mannequin for his or her area and see correlated efficiency,” Lambert stated.
Lambert famous that benchmarks like RewardBench provide customers a strategy to consider the fashions they’re selecting primarily based on the “dimensions that matter most to them, reasonably than counting on a slender one-size-fits-all rating.” He stated the concept of efficiency, which many analysis strategies declare to evaluate, could be very subjective as a result of a very good response from a mannequin extremely relies on the context and targets of the consumer. On the similar time, human preferences get very nuanced.
Ai 2 launched the primary model of RewardBench in March 2024. On the time, the corporate stated it was the primary benchmark and leaderboard for reward fashions. Since then, a number of strategies for benchmarking and enhancing RM have emerged. Researchers at Meta’s FAIR got here out with reWordBench. DeepSeek launched a new approach known as Self-Principled Critique Tuning for smarter and scalable RM.
How fashions carried out
Since RewardBench 2 is an up to date model of RewardBench, Ai2 examined each present and newly skilled fashions to see in the event that they proceed to rank excessive. These included quite a lot of fashions, comparable to variations of Gemini, Claude, GPT-4.1, and Llama-3.1, together with datasets and fashions like Qwen, Skywork, and its personal Tulu.
The corporate discovered that bigger reward fashions carry out finest on the benchmark as a result of their base fashions are stronger. Total, the strongest-performing fashions are variants of Llama-3.1 Instruct. By way of focus and security, Skywork information “is especially useful,” and Tulu did properly on factuality.
Ai2 stated that whereas they imagine RewardBench 2 “is a step ahead in broad, multi-domain accuracy-based analysis” for reward fashions, they cautioned that mannequin analysis ought to be primarily used as a information to choose fashions that work finest with an enterprise’s wants.
