Pareto optimization
[COLM'26] SkillLearnBench is the first benchmark for evaluating continual learning methods that automatically generate agent skills.
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Identifying the Pareto frontier in multi-objective optimization.
SKILL.md
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Logic
A point is Pareto-optimal if no other point dominates it. In this context, we want to maximize F1 and minimize delta. Point A dominates point B if F1_A >= F1_B and Delta_A <= Delta_B (with at least one inequality strict).
def get_pareto_frontier(points):
# points: list of (f1, delta, ...)
# Return points that are not dominated
pareto = []
for i, p1 in enumerate(points):
is_dominated = False
for j, p2 in enumerate(points):
if i == j: continue
# Check if p2 dominates p1 (p2 has better or equal f1, lower or equal delta)
if p2[0] >= p1[0] and p2[1] <= p1[1]:
# If they are strictly better in one dimension, it dominates
if p2[0] > p1[0] or p2[1] < p1[1]:
is_dominated = True
break
if not is_dominated:
pareto.append(p1)
return pareto